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Irregular Attendance of University Students at Class and its Relation to their Academic Achievement

Tribhuvan University Journal

The paper intends to examine the irregular attendance of students in their class and its relation to their academic achievement in five central campuses of Mid- Western University. This study followed descriptive study based on quantitative and qualitative data. Quantitative data were obtained from 172 students selected by non-proportional stratified sampling. Qualitative data were obtained from the campus chiefs, heads of instruction committees and teachers of the central campuses selected purposively. A mixed questionnaire was employed for quantitative data and open ended questionnaire was used to collect qualitative data. The study showed that near about half portion of respondents responded that they were sometimes irregular in their class. Few students (4.45%) who were never irregular belonged to the category of having knowledge of irregular attendance. Higher portion (29.57%) of the male students were always irregular than the female students. The high portion of Master's...

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Azharsyah Ibrahim

This study was motivated by the polemic on the importance of class attendance in a learning process in the institutions of higher education. This article aims to examine the influence of class attendance on students’ grades using quantitative approach. Primary data was collected from four classes of Department of Islamic Banking Diploma (D-III PS) and Department of Islamic Economics of State Islamic University (UIN) Ar-Raniry, Banda Aceh. Data was then analysed using several stastistical techniques from the SPSS. The result showed that statistically there was a significant positive correlation between class attendance and students’ grades. The findings clearly demonstrated the importance of class attendance in attaining the good grades among university students within the two departments. Nevertheless, analyzing the results more closely indicated that class attendance was not a direct factor in enhancing the students’ grades. It only functioned as a mediator and trigger for the emer...

International e-journal of educational studies

Munir Sahin

Mediterranean Journal of Educational Research

It is an important element that students should attend the class regularly to be successful in all educational institutions practicing formal education. The aim of the research is to stduy the relation between the academic achievement and class attendance rank of 46 preservice teachers attending the 4th grade of the department of science education in primary education in Pamukkale University. This reserach in which the mixed method was used adopted both quantitative adn qualitative research techniques. In this research survey model is adopted from the point of quantitative method and case study model is adopted from the point of qualitative method. The paradigm of the reserach from the point of quantitative method is constituted by science preservice teachers taking astronomy lessons. Intentional sample selection is adopted in the reserach. The data of the reserach gathered adopting the technique semi-structured interview techniques from the point of qualitative method. The semi-structured interviews are recorded by tape recorder, then, the research data is observed as thematic turning the records into detailed written documentary and content is analysed. Either directly or indirectly, both intramaturally and extramaturally,education is a process of assisting individuals edify and acquire knowledge,skill and understanding essential for having a part in community life. School is an educational organisation in which students are made obtain terminal behaviours,knowledge and skill in accordance with fundamentals and general and special purposes of the education system through scientific methods (Demirtas and Gunes,2003,s.109). Attendance of students must be implemented in order that the school can carry out its functions. Active participation principle is carried out thorugh the implemention of students' attendance. Education oriented motivation level of students attending classes increases when active participation is used as a base. Academic achievement is defined as students' achievement level of the intended behaviours in school life(Silah, 2003, s. 103). Academic achievement in higher education is used as a criterion in determining whether the students obtained the terminal behaviours or not. Besides, higher education is a significant criterion in job and academic career applications later on. In brief, ensuring the students' attendance increases the academic achievements and the increase in students' achievements can insure their getting a good job, having an academic career and leading a comfortable life. It is observed in literature that limited study has been done related to attendance and absenteeism. These studies can be summarized like the followings; Kadı(2000) searched the constant absenteeism reasons of secondary school students in Adana. In his research Pehlivan(2006) extracted three different results consisting of reasons arise from students; from students' pedagogical conditions and from students' private conditions. In a research named "the reasons for the absenteeism of students and the reflection of absenteeism in academic achievement" Altınkurt(2008) has reached 6 different results arising from personal reasons, academic _____________

steeven espiritu

Meshal Pervaiz

In this study, the research team studied the effects of student's attendance on academic performance; with the major objective of the study is to investigate the relationship between student attendance and academic performance and to examine factors that affect student attendance at SIMAD University. Sample size of 100 students was selected from SIMAD University students, especially faculty of Business and Accountancy, last semester students. Both primary and secondary data was used in order to answer research questions. Questionnaire and content analysis were used as research instrument. The study found that there is a moderate positive relationship between student attendance and academic performance. Based on the findings, the researchers suggest that all students, particularly prospective students and those students who are not as academically strong, to be informed about the importance influence of class attendance on academic performance. And also the study recommended that universities should maintain or develop strict guidelines for student attendance and monitor factors that could hinder a student from attending class on a regular basis.

European of Social …

Dr. Azizi Yahaya

Procedia - Social and Behavioral Sciences

Quality & Quantity

Pilar Aparicio-Chueca , Maribel Cebollero

Journal of Social Sciences

Jamaludin Ramli

In this study, the research team studied the effects of student’s attendance on academic performance; with the major objective of the study is to investigate the relationship between student attendance and academic performance and to examine factors that affect student attendance at SIMAD University. Sample size of 100 students was selected from SIMAD University students, especially faculty of Business and Accountancy, last semester students. Both primary and secondary data was used in order to answer research questions. Questionnaire and content analysis were used as research instrument. The study found that there is a moderate positive relationship between student attendance and academic performance.

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Students’ intelligence test results after six and sixteen months of irregular schooling due to the COVID-19 pandemic

Moritz breit.

1 Department of Psychology, University of Trier, Trier, Germany

Vsevolod Scherrer

Joshua blickle.

2 Department of Psychology, Chemnitz University of Technology, Chemnitz, Germany

Franzis Preckel

Associated data.

Both data files are available from the OSF-database ( https://osf.io/xseh3/ ).

The COVID-19 pandemic has affected schooling worldwide. In many places, schools closed for weeks or months, only part of the student body could be educated at any one time, or students were taught online. Previous research discloses the relevance of schooling for the development of cognitive abilities. We therefore compared the intelligence test performance of 424 German secondary school students in Grades 7 to 9 (42% female) tested after the first six months of the COVID-19 pandemic (i.e., 2020 sample) to the results of two highly comparable student samples tested in 2002 ( n = 1506) and 2012 ( n = 197). The results revealed substantially and significantly lower intelligence test scores in the 2020 sample than in both the 2002 and 2012 samples. We retested the 2020 sample after another full school year of COVID-19-affected schooling in 2021. We found mean-level changes of typical magnitude, with no signs of catching up to previous cohorts or further declines in cognitive performance. Perceived stress during the pandemic did not affect changes in intelligence test results between the two measurements.

Introduction

The ongoing COVID-19 pandemic and the associated countermeasures have caused many temporary but far-reaching changes in societal processes around the world, affecting work, culture, social life, and education. Like many other institutions, schools were often unprepared for a fundamental change and partial shutdown of their operations, leading to a prolonged period of improvised forms of teaching and school absenteeism around the world [ 1 ]. Many potential consequences of the disruption of normal schooling have been discussed and investigated, including learning loss [ 2 , 3 ], students’ feelings and mental health [ 4 , 5 ], and students’ experiences and attitudes toward online learning [ 6 , 7 ]. Little is known about the effects of the pandemic on students’ intelligence. It has been hypothesized that general increases in stress and anxiety during the pandemic limit cognitive functioning [ 8 ]. Moreover, academic achievement and intelligence have previously been shown to be highly interdependent [ 9 ], with strong positive effects of schooling on intelligence test performance [ 10 , 11 ], This suggests that a prolonged disruption of regular schooling may also cause deficits in intellectual performance. In the present study, we therefore investigated the impact of the pandemic on intelligence test performance in a sample of German secondary school students. The results may provide some practical guidance on whether educational compensatory measures are needed and whether the consequences of the pandemic need to be considered in post-pandemic intelligence assessments.

Schooling and intelligence

Schooling is a central predictor of many important outcomes, such as health [ 12 ], income [ 13 ], and intelligence [ 11 ]. Intelligence can be modeled as a hierarchy of multiple cognitive abilities of different generality [ 14 ]. Key aspects of intelligence are the capacities for information processing, problem solving, and abstract reasoning [ 15 ]. According to Linda Gottfredson [ 16 p13], “[Intelligence]… involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—“catching on,” “making sense” of things, or “figuring out” what to do.” Crucially, intelligence, albeit being mostly discussed as a predictor of school achievement [ 17 ], has also been shown to be significantly impacted by schooling. For example, an increase in compulsory schooling in Norway from 7 to 9 years significantly increased the average intelligence quotient (IQ) [ 18 ]. The effect of one additional year of schooling was quantified by different studies between 1 and 10.8 IQ points [ 10 , 11 ]. In a review, Rindermann [ 19 ] found an average positive effect of 5.6 IQ points per year of schooling in Germany. In a meta-analysis of 42 studies, Ritchie and Tucker-Drob [ 11 ] reported a benefit of 1 to 5 IQ points per year of schooling.

Based on these and other findings, Peng and Kievit [ 9 ] proposed a bidirectional perspective on the development of academic achievement and intelligence. They argued that in line with mutualism theory and the transactional model [ 20 , 21 ], intelligence and academic achievement show positive reciprocal relations throughout development, leading to an increasingly strong association. Thus, any interventions targeting schooling should also affect intelligence [ 9 ]. A recent meta-analysis supported the suggested bidirectional longitudinal relations [ 21 ]. Schooling is hypothesized to be an important mechanism behind this bidirectionality [ 9 ]. During schooling, students invest their cognitive abilities to acquire academic skills and to perform academic tasks, which in turn involves the use of cognitive abilities; thus, schooling offers a long-term training for cognitive abilities. Sustained and high-quality schooling therefore should have direct positive effects on student’s academic and cognitive development as well as indirect effects by triggering cognitive-academic bidirectionality [ 9 ]. Further, students’ family socio economic status (SES) influences relations between students’ intelligence and academic achievement due to better early learning opportunities for children with a high family SES [ 21 ].

In light of the pandemic, the findings on the importance of schooling for intelligence development are troubling with regard to the global generation of students affected by prolonged school closures and irregular school attendance. Many researchers fear an increased number of school dropouts and reduced graduation rates in secondary education [ 22 ]. The expected effect of the pandemic on intelligence levels is difficult to quantify, as remote schooling does not equate to complete school absence. However, a lower quality of schooling by remote schooling for which teachers were not prepared in addition with a reduced time investment in education [ 2 ] over many months may still be very noticeable in intelligence test results.

Schooling during the pandemic in Germany

The present investigation was conducted with secondary school students from the German federal state of Rhineland-Palatinate. We first describe the school-related measures for the first six months of the pandemic (March-August 2020), leading up to the first measurement point. Second, we describe the measures for the next ten months, constituting the full 2020/2021 school year and leading up to the second measurement point. The full timeline for secondary school students is illustrated in Fig 1 .

The 2019/2020 school year (second half)

On March 13, 2020, all schools closed by order of the state government [ 23 ]. On May 25, Grades 3 to 6 returned to school; on June 8, the rest of the students returned. During this time, students mostly alternated between remote schooling and small group lessons, as the number of students allowed in a classroom was limited to half class size [ 24 ]. The summer break was from July 6 to August 14, after which all students returned to school in mostly regular operation. Taken together, the majority of students did not attend school for three months while receiving assignments to be completed at home, returned to school under strict regulations or only in small groups for four weeks before going on a summer break for six weeks. During the three months of remote schooling, many students greatly reduced their time investment in education. On average, students spent only half of their usual time on daily educational activities, with 38% of secondary school students reporting less than 2 hours of schoolwork per day [ 25 ].

The 2020/2021 school year

The 2020/2021 school year started with regular schooling until December 16, when the Christmas break started early due to the pandemic situation [ 26 ]. After the break ended on January 4, remote schooling was conducted until February 14 for Grades 5 and 6 and until March 15 for Grades 7 to 13 [ 27 , 28 ]. Students then alternated between remote schooling and small group lessons until June 14, when regular schooling returned [ 29 ]. Taken together, the students attended school regularly for four months, went on Christmas break for three weeks, did not attend school for three months while receiving assignments to be completed at home for ten weeks, returned to school under strict regulations or only in small groups for nine weeks, before returning to regular schooling.

The effects of the pandemic on students

The disruption of schooling and many other aspects of everyday life as well as the uncertainty and threatening nature of the pandemic situation have affected students psychologically in many ways. Camacho-Zuñiga et al. [ 4 ] investigated the emotional state of over 4,000 Mexican school and university students, finding low energy levels and negative feelings such as anxiety, stress, and tiredness to be prevalent during periods of lockdown. Similar results were found in other countries [ 30 , 31 ]. Students’ feelings toward remote teaching were examined by Niemi and Kousa [ 7 ] in Finland, finding that online teaching was generally implemented successfully but also led to fatigue and loss in motivation in a portion of students. In addition to these emotional and motivational costs of the pandemic, many psychologists and educators also predicted a severe learning loss at the start of the pandemic [ 25 , 32 ]. Students reported spending only approximately half the usual daily time on educational activities [ 25 , 33 ]. This effect was especially strong in low-achieving students [ 33 ] and students from low-income families [ 34 ]. In line with earlier predictions, Engzell et al. [ 2 ] reported a learning loss of d = .08 after only eight weeks of lockdown in the Netherlands. In the US, gains in reading and math were 3 to 12 percentile points lower in the 2020–21 school year than in previous years [ 35 ]. Hammerstein et al. [ 3 ] reviewed the available literature on learning loss during the pandemic. They found that a majority of studies reported evidence for learning loss, with median effects of d = -.10 for math and d = -.09 for reading.

Early in the pandemic, Boals and Banks [ 8 ] warned that losses in cognitive performance were also to be expected in both children and adults. They argued that increases in stress and anxiety would cause mind wandering and worrying because of the constant stream of news on the issue and worry about oneself and others [ 36 ]. Mind wandering can be defined as thoughts about concerns that are unrelated to the task at hand and it takes up limited resources of executive functioning, potentially impairing any cognitive performance [ 37 ]. The pandemic brought numerous stressors for many students, such as increased tension at home, loss of social contact with peers, or worries related to safety and health; [ 38 ] further, it led to a loss of resources like physical activities, which help to reduce negative stress effects [ 39 ]. However, the effects of stress on cognitive performance are not well understood. There is some evidence that increased stress leads to lower cognitive functioning, but effects differed between different cognitive abilities and the research is largely based on acute stress instead of long-term elevated stress [ 40 – 43 ]. Thus, it is difficult to make precise predictions about the effects of pandemic-induced stress on cognitive performance on the basis of these findings.

Some studies have examined the effect of the pandemic on cognitive performance. Podlesek et al. [ 36 ] surveyed 830 Slovenian adults during the first wave and lockdown regarding their emotions (e.g., stress, anxiety, fatigue) and perceived changes in cognition. Participants reported mildly impaired cognitive functioning. The level of perceived impairment was significantly predicted by stress and negative emotions. Castanheira et al. [ 44 ] tested 1,517 American adults with a battery of executive functioning tasks and asked about pandemic-related worry and stress. They compared the results to a sample that was tested before the pandemic, finding that executive functioning was generally lower in the pandemic sample than in the prepandemic sample and that worry negatively predicted executive functioning in the pandemic sample. However, this research has so far been limited to adults; comparable findings for children and adolescents are missing.

The present study

The COVID-19 pandemic and the associated disruption of regular schooling have negatively affected students in many ways. However, the impact on intelligence test performance has not yet been investigated. Previous studies [ 36 , 44 ] focused on adult samples, were limited to self-reports or measures of executive functioning and only drew on cross-sectional data. Therefore, the present study investigated the intelligence test performance of German secondary school students during the pandemic. Like most other research on the effects of the pandemic, we faced some challenges. The unpredictability of the pandemic precluded the anticipatory launch of a longitudinal study with measurement time points prior to the onset of the pandemic. In addition, the uniform impact of the pandemic on all students prevented the use of a control group design. We compensated for these issues in two ways. First, two prepandemic samples were available that had been tested with the same intelligence test. These samples were used to create highly comparable comparison groups using propensity score matching. Second, the ongoing effects of the pandemic were assessed by retesting the pandemic sample after one full school year.

The pandemic sample assessed in 2020 comprised students from regular classes and special classes for gifted students. We conducted two sets of analyses. First, we compared the intelligence test results of the pandemic sample tested in August or September 2020 to the results of two comparable samples tested in 2002 and 2012. In Analysis 1a, we compared two propensity score matched subsamples from the pandemic 2020 sample and the prepandemic 2002 sample, comprising students from both regular classes and special classes for the gifted. The 2002 data stem from the norming sample of the intelligence test used. In Analysis 1b, we compared three propensity score matched subsamples from the pandemic 2020 sample and the prepandemic 2012 and 2002 samples. Samples in Analysis 1b only comprised students from special classes for the gifted because the 2012 sample did not include students from regular classes. The 2020–2002 comparison (Analysis 1a) is therefore more representative for the entire student body. By comparing the 2020, 2012, and 2002 samples (Analysis 1b), we investigated if any observed differences in Analysis 1a are better interpreted as part of a more continuous development of test scores from 2002 to 2012 to 2020. We expected to find significantly lower intelligence scores for the 2020 sample in both analyses and no decreasing trend between 2002 and 2012, as no decreasing intelligence levels have generally been observed during this time in Germany [ 45 ].

In Analysis 2, we investigated the mean level change in intelligence after one school year in the pandemic 2020 sample (retest in 2021). Perceived stress during this school year was also assessed. We expected a decline in intelligence test scores when taking typical retest effects into account. That is, we expected a decline in scores or at least smaller increases than the positive retest effects expected based on meta-analytic evidence on retest effects in intelligence testing [ 46 ]. Furthermore, based on the predictions made by Boals and Banks [ 8 ], we investigated whether the level of perceived stress could explain changes between the two measurements. That is, we investigated if stress was a significant negative predictor of latent change scores of cognitive abilities.

Methods and materials

Participants, 2020 sample (pandemic).

A total of 424 students from Grade 7 (34.67%), 8 (33.25%), and 9 (32.08%) were tested in late August or early September 2020 with the Berlin Structure-of-Intelligence Test (BIS-HB) [ 47 ]. The students attended either regular classes or special classes for the gifted (45.75%) (schools offered both class types) in four German grammar schools in Rhineland-Palatine. The mean age was 13.34 years ( SD = .99), and 41.98% of the sample identified as female. Of the sample, 98 students were too young or too old to receive a norm-referenced IQ score from the intelligence test (norms for ages 12.5–16.5 years) and were excluded from the analyses. Note that for additional 24 participants, some of the intelligence scales were not available because they did not complete some of the corresponding tasks according to the instructions or were absent for a short period of time during the testing. These 24 students with missing values did not significantly differ from the 302 students without missing values regarding age (mean age = 13.66 vs. 13.80 years; T = .69, p = .499), gender (missing percentage: 8.00% of males vs. 6.60% of females; Chi 2 = .23, p = .631), and grade level (missing percentage: 7.80% of Grade 7 vs. 6.80% of Grade 8 vs. 7.00% of Grade 9; Chi 2 = .29, p = .962). However, missing values were related to class type ( Chi 2 = 5.58, p = .018): Students from regular classes were more likely to have missing scores than students form special classes for the gifted (missing percentage: 10.40% vs. 3.50%). We only excluded students with missings on all variables from the analyses. Sample sizes and demographic variables for the unmatched and matched samples are presented in Table 1 .

a = students attended a special class for the gifted (selection of students for these classes is based on intelligence tests, school achievements, and teacher observations in trial lessons).

b = 24.9% of the sample attended Grades 5, 6, or 10.

Of the 326 students with an IQ score at the first measurement point, 257 (78.83%) were retested in July 2021. The 69 students missing at the second time of measurement did not significantly differ from the 257 students that were retested with regard to age (mean age = 13.67 vs. 13.61 years; T = .55, p = .581), gender (missing percentage: 21.50% males vs. 21.20% females; Chi 2 = 1.90, p = .387), class type (missing percentage: 22.50% regular classes vs. 19.40% gifted classes; Chi 2 = 4.58, p = .499), and grade level (missing percentage: 23.40% Grade 7 vs. 19.70% Grade 8 vs. 21.50% Grade 9; Chi 2 = .38, p = .827).

Note that for additional 17 participants, some of the intelligence scales were not available at T2 because they did not complete the corresponding tasks correctly or were absent for a short period of time during the testing. These 17 students did not significantly differ from students without any missing values at T2 regarding age (mean age = 13.61 vs. 13.88 years; T = 1.35, p = .179), gender (missing percentage: 6.90% males vs. 3.70% females; Chi 2 = 2.05, p = .359), class type (missing percentage: 6.90% regular classes vs. 3.50% gifted classes; Chi 2 = .19, p = .186), and grade level (missing percentage: 4.30% Grade 7 vs. 5.60% Grade 8 vs. 5.80% Grade 9; Chi 2 = .22, p = .896). We only excluded students with missings on all variables.

All parents of the participants gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the principals of the participating schools. The data collections were approved by the Supervision and Services Directorate of Rhineland-Palatinate on the basis of ethical and data protection requirements (Aufsichts- und Dienstleistungsdirektion; protocol numbers 153–20 and 226–21).

2012 sample (prepandemic)

A total of 197 Grade 8 students who attended the same four schools as the 2020 sample were tested between 2011 and 2013 (“2012 sample” for short) with the BIS-HB. All students attended special classes for the gifted. The mean age was 13.87 years ( SD = .48), and 41.42% of the sample identified as female. Sample sizes and demographic variables for the full sample and matched samples are presented in Table 1 . There were no missing data. All parents of the participants gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the principals of the participating schools. The data collection was approved by the Supervision and Services Directorate of Rhineland-Palatinate on the basis of ethical and data protection requirements (Aufsichts- und Dienstleistungsdirektion; protocol number 32–03 405/29/05).

2002 sample (prepandemic)

1506 Grade 5 to 10 students attending schools in five German federal states were tested in 2002 in the context of the BIS-HB standardization [ 47 ]. These students were distributed across all German school tracks, with a subset of 571 students attending regular classes in grammar schools and 450 students attending special classes for the gifted in grammar schools. The mean age was 14.54 years ( SD = 1.35), and 44.62% of the sample identified as female. Sample sizes and demographic variables for the full sample and matched samples are presented in Table 1 . There were no missing data. All parents of the participants gave written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the principals of the participating schools. The data for this study was collected by order of and in accordance with the recommendations of the German Federal Ministry of Education and Research. The present study represents a secondary analysis of this dataset.

Intelligence

The BIS-HB [ 35 ] is a paper-and-pencil intelligence test designed to capture the intelligence structure of above-average and high-ability adolescents. It can also be applied for testing average and below-average ability individuals. The test is based on the Berlin Model of Intelligence Structure (BIS) by Jäger [ 48 ]. The BIS is a faceted model of intelligence ( Fig 2 ). The operation facet includes processing speed [S], memory [M], creativity [C], and reasoning [R]. The content facet includes verbal [V], numerical [N], and figural [F] ability. Each individual test item is assigned to a combination of one operation and one content (e.g., a verbal processing speed task). Thus, each operation score is a combination of verbal, numerical, and figural tasks of the respective operation, and each content score is a combination of speed, memory, creativity, and reasoning tasks of the respective content. On a higher hierarchical level, the abilities from the operation facet and from the content facet are integrated into general intelligence. The BIS-HB comprises 45 tasks assessing the four operations and three domains, providing eight test scores (S, M, C, R, V, N, F, and g ). The test was used in all samples and at both measurement points of the pandemic sample.

f = Figural Ability, v = Verbal Ability, n = Numerical Ability, r = Reasoning, s = Processing Speed, m = Memory, c = Creativity.

The construct validity of the BIS-HB has been documented by confirmatory factor analyses (multiple group comparisons for the different age and ability groups; range of CFIs = .97-.99); criterion validity has been documented by correlations with other intelligence tests (e.g., BIS-HB reasoning with the German version of the culture fair test [ 49 ]: r = .74, N = 1080; BIS-HB creativity with a verbal creativity test [VKT; 50 ]: r = .52, N = 146) and school grades (BIS-HB IQ with grade point average: r = .50, N = 1320; BIS-HB reasoning with grade point average in Math and sciences: r = .47, N = 1313) [ 47 ]. The operations of the BIS are conceptually close to corresponding abilities in the Cattell-Horn-Carroll model (CHC-model) [ 14 ]. Processing speed is included in both models, memory is similar to learning efficiency in the CHC-model, creativity to retrieval fluency, and reasoning to fluid reasoning. This proximity to another established structural model of intelligence makes it unlikely that the results of the current study are limited to the scales of the BIS.

Perceived stress

At the second measurement point of the pandemic sample, we included a three-item scale assessing the perceived stress and changes in well-being caused by the disruption of regular schooling and the pandemic. The scale was adapted from the School Barometer in Germany, Austria and Switzerland, [ 51 ] and the answer format was a 5-point Likert scale. The three questions were as follows (translation by authors):

• I was doing well with the school closures.
• I was doing well with the alternating lessons.
• I find the "coronavirus situation" stressful.

We conducted two separate analyses, one comparing the 2020 sample to the 2002 sample (Analysis 1a) and one comparing the 2020 sample to both the 2012 and 2002 samples (Analysis 1b). Before each analysis, we conducted propensity score (PS) matching to create comparable subsamples. Then, we tested the differences in intelligence between the resulting subgroups using MANOVA, ANOVA, and Discriminant Function Analyses.

Propensity score matching . We used PS matching with the matchIt package in R to control for demographic differences between the 2020 sample and the 2002 sample (Analysis 1a) and between students from special classes for the gifted from the 2020 sample, the 2012 sample, and the 2002 sample (Analysis 1b). Propensity score matching is a method in which individuals from one group are matched to individuals from a second group based on the calculated PS for each person [ 52 ]. The PS represents the probability of assignment to a particular group based on a vector of observed covariates [ 53 ]. Thus, by ensuring that two groups do not differ in their PS, one controls for potential a priori differences between the groups on the observed covariates.

In Analysis 1a, students from the 2002 sample were matched to students from the 2020 sample using the nearest neighbor algorithm [ 54 ], based on age as a continuous covariate as well as sex, grade level, and class type that had to match exactly. In Analysis 1b, students from regular classes were excluded from the 2020 and 2002 samples because no such students were available in the 2012 sample. In two separate matching procedures, students from the 2020 sample and then students from the 2002 sample were matched to students from the 2012 sample using the nearest neighbor algorithm, based on age as a continuous covariate as well as sex that had to match exactly.

In all conducted PS matching procedures, we applied the recommended caliper of .20 [ 55 ] and allowed the algorithm to discard cases from both groups. As a criterion to evaluate the quality of the PS matchings we calculated Hedges’ g effect sizes for differences of the propensity score and age between the matched samples (Hedges’ g = M 1 - M 2 S D _ p o o l e d ). A Hedges’ g smaller than .20 was interpreted as a negligible difference. In addition, we conducted the overall balance test [ 56 ] and L1 statistics [ 57 ] that both test whether the matched samples differ on all covariates combined. L1 values can range from 0 to 1 with 1 indicating a total imbalance between the samples and 0 indicating zero differences between the matched samples.

ANOVA , MANOVA and discriminant function analysis . In both Analyses 1a and 1b, we performed a multivariate analysis of variance (MANOVA) using the seven BIS-HB specific ability scores as dependent variables. In case that one MANOVA indicated a significant multivariate main effect, we conducted a discriminant function analysis as a post hoc test to find out which particular intelligence scales discriminate between the samples. Note that the BIS-HB g -factor was excluded from the MANOVA because it represents the sum of all specific ability scores. We conducted one additional ANOVA including the BIS-HB g -factor as the dependent variable. As a precondition for MANOVA we conducted several tests. First, for multivariate normality [ 58 ], we estimated Marida’s multivariate skewness and kurtosis [ 59 ] and Royston’s extension of the Shapiro–Wilk test [ 60 ]. Second, to identify multivariate outliers, we obtained Mahalanobis distances [ 61 ] and calculated the respective Chi-squared test for each participant. Third, the Levene test [ 62 ] for equality of variances and the Box-test [ 63 ] for equality of covariance matrices were conducted. In case of a significant violation of the preconditions of MANOVA, we conducted non-parametric robustness check analyses in the form of permutation-based multivariate analysis of variance (PERMANOVA) using the R package ‘vegan’ [ 64 ]. As preconditions for ANOVA, we tested the homogeneity, homoskedasticity, and the univariate normality of the g -factor by the Levene test, the Breusch Pagan test, and the Kolmogorov-Smirnov test, respectively. In case of a significant violation of any preconditions, we conducted non-parametric robustness check analyses in the form of the Kruskal-Wallis test in SPSS.

Analysis 2. Mean-level change . We investigated the effect size and statistical significance of mean-level change in all eight BIS-HB ability scores by computing Hedges’ g [ 65 ]. We compared the results to the average retest effects reported in a recent meta-analysis [ 46 ].

Latent change score analysis . We calculated eight latent change score (LCS) structural equation models (SEM) to test whether perceived stress significantly predicted changes in the BIS-HB ability scores. All SEMs were calculated in Mplus version 8.4 [ 66 ] by using the maximum likelihood estimator with robust standard errors (MLR) [ 67 ]. We applied the “type is complex” option to account for the nested data structure (“students within classes”). Missing data was handled by using the full information maximum likelihood algorithm [ 66 ]. In each SEM, a latent change score of either the general intelligence or one specific ability score was estimated based on the T1 and T2 measurements of intelligence. As an example, we present the SEM based on general intelligence in Fig 3 . The T1 intelligence measure as well as the LCS predict the T2 intelligence measure with a fixed value of 1. That is, the T2 measure is completely determined by the first measure and the change value. The LCS is a latent variable that depicts interindividual differences in the change in students’ intelligence between T1 and T2. In each SEM, perceived stress was included as a predictor of the LCS. That is, interindividual differences in the intraindividual intelligence change over time were predicted by students’ perceived stress.

LCS = Latent change score. g_t1 = General Intelligence at the first measurement point. g_t2 = General Intelligence at the second measurement point.

Analysis 1a: 2002 vs. 2020

The PS matching algorithm in Analysis 1a matched 104 students from the 2002 sample to 104 students from the 2020 sample. Descriptive differences on all covariates before and after the PS matching are presented in Table 1 . The matched samples showed exact same proportions of gender, class type, and grade level, as well as negligible differences in age (mean age = 13.64 vs. 13.65 years; Hedges’ g = .01) and PS (mean PS = .46 vs. .45; Hedges’ g = .04). Hedges’ g , percentage of the propensity score overlap, the overall balance test, and L1 statistics before and after the matching procedure are reported in Table 2 . The PS distribution of the two groups before and after matching is depicted in Fig 4 .

Unmatched units were discarded after the matching procedure to ensure an optimal overlap in propensity scores of the two groups.

Note . PS = Propensity Score.

The BIS-HB ability score means for the matched 2002 and 2020 samples are presented in Table 3 and Fig 5 . The MANOVA revealed a large, statistically significant difference between the samples in their BIS-HB results in favor of the matched 2002 sample ( F [7, 200] = 6.881, p < .001, partial η 2 = .194). The discriminant function analysis indicated a significant function (i.e., Function 1) that differentiated between the 2002 sample and the 2020 sample (Eigenwert = .26, Wilks-Lambda = .80, Chi 2 = 46.016, df = 7, p < .001). The structural coefficients of this function are presented in Table 4 . All intelligence scales except for creativity showed substantial structural coefficients ( r ranged from .38 in N to .79 in M) indicating meaningful differences on these scales between the 2002 sample and the 2020 sample in the favor of the 2002 sample. Creativity indicated virtually no relation ( r = -.02) with the calculated function and thus did not discriminate between the samples. Finally, the ANOVA revealed a medium difference between the samples in the g -factor in favor of the matched 2002 sample ( F [1, 206] = 15.677, p < .001, partial η 2 = .071).

Note . g = General Intelligence, S = Processing Speed, M = Memory, C = Creativity, R = Reasoning, F = Figural Ability, N = Numerical Ability, V = Verbal Ability

Marida’s multivariate skewness and kurtosis tests indicated a significant violation of the multivariate normality in the MANOVA (Marida’s skewness = 633.70, p < .001; Marida`s kurtosis = 18.41, p < .001). Royston’s test indicated no significant violation of the multivariate normality (H = 9.12, p = .167). Chi-squared tests based on Mahalanobis distances indicated eleven significant outliers ( p > .05) that were checked and not attributed to coding errors. The robustness check in the form of PERMANOVA supported the MANOVA results, indicating a significant difference between the groups (R 2 = .06, p = .002).

The Levene test ( F based on means = .74, df = 206, p = .392), the Breusch Pagan test (Chi-square = .95, df = 1, p = .330), and the Kolmogorov-Smirnov test (K = .06, df = 208, p = .200) indicated no violation of the homogeneity, homoskedasticity, and normality of the g -factor, respectively. Therefore, no non-parametric robustness check analyses were conducted in addition to the ANOVA.

Analysis 1b: 2002 vs. 2012 vs. 2020

The PS matching algorithm matched 113 students from the 2012 sample to 113 students from the 2020 sample. A second PS matching algorithm matched 110 students from the sample 2002 to the 110 students from the 2012 sample. Finally, a third PS matching procedure matched 110 students from the 2012 sample that were chosen by the second PS matching to 110 students from 2020 sample (i.e., three students from the first PS matching were discarded because they did not have a match in the second PS matching). Descriptive differences on all covariates before and after the PS matching are presented in Table 1 . The matched samples showed the same proportions in gender and class type, as well as negligible differences in age (mean age = 13.74 vs. 13.76 vs. 13.76 years; average Hedges’ g = .03) and PS (mean PS = .43 vs. .43 vs. 43; average Hedges’ g = 0). They differed in grade level as only Grade 8 students were available in the 2012 sample and the covariate could therefore not be taken into account in the matching procedure. The PS distribution of the first and the second PS matchings in Analyses 1b are presented in Figs ​ Figs6 6 and ​ and7, 7 , respectively.

The BIS-HB ability score means for the matched 2002, 2012, and 2020 samples are presented in Table 5 and Fig 8 . The MANOVA revealed a medium-sized, statistically significant difference between the samples in their BIS-HB results ( F [14, 644] = 4.409, p < .001, partial η 2 = .087). The discriminant function analysis indicated two significant functions that differentiated between the 2002 sample, the 2012 sample, and the 2020 sample (Function 1: Eigenwert = .12, Wilks-Lambda = .83, Chi 2 = 59.41, df = 14, p < .001; Function 2: Eigenwert = .08, Wilks-Lambda = .93, Chi 2 = 24.03, df = 6, p < .001). Function 1 and Function 2 explained 60% and 40% of the variance, respectively. The structural coefficients of both functions are presented in Table 6 . In Function 1, all intelligence scales showed substantial structural coefficients ( r ranged from .31 in N to .87 in V). This function discriminated between the 2012 sample on one hand and between the 2002 sample and 2020 sample on the other hand indicating higher intelligence scores in the 2012 sample than in the other two samples. In Function 2, all intelligence scales showed substantial structural coefficients ( r ranged from .30 in F to .82 in M) except for C ( r = -.04) and R ( r = .17). Function 2 mainly differentiated between the 2002 sample and the 2020 sample indicating higher intelligence scores in the 2002 sample. Finally, the ANOVA revealed a medium difference between the samples in the g -factor score ( F [2, 327] = 12.359, p < .001, partial η 2 = .070). Tukey post-hoc-tests indicated higher g -factor scores in the 2012 sample than in the 2002 sample ( p = .002) and the 2020 sample ( p < .001). The 2002 sample and the 2020 sample did not differ significantly in the g -factor score ( p = .375).

Marida’s multivariate skewness and kurtosis tests indicated a significant violation of the multivariate normality (Marida’s skewness = 248.64, p < .001; Marida`s kurtosis = 5.87, p < .001). Similarly, Royston’s test indicated a significant violation of the multivariate normality (H = 26.68, p < .001). Chi-squared tests based on Mahalanobis distances indicated 12 significant outliers ( p > .05) that were checked and not attributed to coding errors. In addition, the Levene test for equality of variances and the Box-test for equality of covariance matrices were significant ( p < .05). We therefore again conducted a PERMANOVA as a robustness check. The results were similar to the MANOVA, also indicating a significant difference between the groups (R 2 = .02, p = .001). Post hoc tests based on the custom R script ‘pairwiseAdonis’ ( https://github.com/pmartinezarbizu/pairwiseAdonis ) indicated that all three groups differed significantly from one another (2002 vs. 2012: R 2 = .04, p = .003; 2002 vs. 2020: R 2 = .02, p = .048; 2012 vs. 2020, R 2 = .06, p = .003; all p -values Bonferroni-corrected).

The Levene test indicated a violation of the homogeneity in the g -factor between the samples ( F based on means = 5.12, df = 328, p = .024). The Breusch Pagan test indicated a violation of the homoskedasticity in the g -factor between the samples (Chi-square = 4.89, df = 1, p = .027). The Kolmogorov-Smirnov test indicated no violation of normality of the g -factor (K = .05, df = 330, p = .200). We therefore conducted a the Kruskal-Wallis test as a robustness check for the ANOVA. The results were similar to the ANOVA. We observed a significant main effect ( F = 23.67, df = 2, p < .001) and the Bonferroni post-hoc-tests indicated higher g -factor scores in the 2012 sample than in the 2002 sample ( p = .003) and the 2020 sample ( p < .001). The 2002 sample and the 2020 sample did not differ significantly in the g -factor score ( p = .461).

Analysis 2: 2020/2021

Mean-level change.

Table 7 shows the mean level change in all BIS-HB ability scores. All scores significantly increased from test to retest. Test-retest correlations ranged from r = .71 (Memory) to r = .87 (General Intelligence). The median increase was 6.86 IQ points (Hedges’ g = .53), ranging from 3.56 IQ points for Creativity (Hedges’ g = .22) to 11.93 IQ points for Processing Speed (Hedges’ g = .90). General Intelligence increased by 7.56 IQ points (Hedges’ g = .59). Fig 9 graphically compares the observed mean-level change to typical mean-level change observed meta-analytically and in a previous investigation using BIS-HB ability scores. There was neither a remarkable decrease in the intelligence test scores over the 2020–2021 school year nor a strong increase that may be interpreted as “catching up” to previous cohorts, as indicated by largely comparable retest effects to the previous investigation and the meta-analysis.

Note . All differences were p < .001 after applying Bonferroni correction.

Latent change score analysis

LCS SEMs indicated that interindividual differences in the intraindividual change of BIS-HB ability scores were not predicted by perceived stress. The standardized parameters for each ability score are reported in Table 8 . The influence of perceived stress did not reach statistical significance for any ability score.

Note . LCS = Latent change score. g = General Intelligence. S = Processing Speed. M = Memory. C = Creativity. R = Reasoning. F = Figural Ability. N = Numerical Ability. V = Verbal Ability.

Intelligence test results were lower in the pandemic 2020 sample than in the prepandemic 2002 and 2012 samples. The differences in test scores were large, with a difference in general intelligence of 7.62 IQ points between 2020 and 2002 (Analysis 1a). This difference did not appear to be a continuation of a longer decreasing trend. In contrast, we observed larger test scores in 2012 than in 2002 but lower scores in 2020. The difference between 2012 and 2020 was also substantial, with a difference in general intelligence of 6.54 points (Analysis 1b). The cross-sectional cohort comparisons therefore seem to corroborate previous results that regular schooling has a substantial impact on intelligence development and its absence is detrimental for intelligence test performance [ 9 ]. The difference in test scores was remarkably large. It may be the case that the student population was hit particularly hard by the pandemic, having to deal with both the disruption of regular schooling and other side effects of the pandemic, such as stress, anxiety, and social isolation [ 68 ]. Moreover, students are usually very accustomed to testing situations, which may be less the case after months of remote schooling.

Creativity scores were notably lower than other scores in 2002. It therefore seems like the nonsignificant difference in creativity between 2002 and 2020 was not due to creativity being unaffected by the pandemic, but instead due to creativity scores being low in 2002. This is supported by significantly higher creativity scores in 2012. Lower creativity in 2002 than in later years may be due to unfamiliarity with the testing format, changes in curricula, or changes in out of school activities.

Importantly, the overall results are inconsistent with one possible alternative explanation of decreasing intelligence test scores, namely, a reverse Flynn effect. Flynn observed a systematic increase in intelligence scores across generations in the 20 th century [ 69 ]. In some countries, a reversed Flynn effect with decreasing intelligence scores across generations has been observed in recent years [ 17 , 70 , 71 ]. This seems to be an especially plausible alternative explanation for the observed differences in test scores in our Analysis 1a. However, there are arguments against this alternative explanation. A reversal of the Flynn effect has not yet been observed in Germany. Instead, even in recent years, a regular positive Flynn effect has been reported [ 45 , 72 ]. Moreover, a reverse Flynn effect is also inconsistent with our observation of increasing test scores from 2002 to 2012. We observed an increase in General Intelligence equivalent to .47 IQ points per year, which is slightly larger than the typically observed Flynn effect [ 73 ] or the Flynn effect observed in Germany [ 45 ]. The observed decrease in test scores from 2012 to 2020 with .82 IQ points per year for General Intelligence is also much larger than the reverse Flynn effect observed elsewhere (.32 IQ points) [ 74 ], making it unlikely that this effect alone could account for the observed decline.

The longitudinal results ( Fig 9 ) showed an increase in test scores between the test (2020) and retest (2021). The magnitude of the increase is in line with the retest effects for intelligence testing that have been quantified meta-analytically ( d = .33) [ 46 ]. In some cases the retest effects were larger than expected based on the meta-analysis (e.g., Processing Speed, Figural Ability). However, these cases were largely in line with a previous investigation of retest effects in a subsample of the BIS-HB standardization sample, [ 75 ] with no clear pattern of consistently larger or smaller retest effects in the present sample. These results indicate neither a remarkable decrease nor a “catching up” to previous cohorts.

Interestingly, we found no impact of perceived stress on the change in intelligence test scores. A possible explanation for the observed results is that stress levels were especially high in the first months of the pandemic, when there was the greatest uncertainty about the nature of the disease and lockdowns and school closures were novel experiences. Some evidence for a spike in stress levels at the beginning of the pandemic comes from tracking stress-related migraine attacks [ 76 ] and from a longitudinal survey of college students that was conducted in April and June 2020, finding the highest stress levels in April [ 77 ]. Moreover, teachers and students were both completely unprepared for school closures and online teaching at the beginning of the pandemic. The retest was conducted after a month-long period of regular schooling, followed by a now more predictable and better prepared switch to remote schooling that did not catch teachers and students off guard entirely. These factors may explain why intelligence performance did not drop further and why stress levels did not have an effect on the change in performance in the second test.

Strengths and limitations

The present study has several strengths. To our knowledge, this is the first investigation of the development of intelligence test performance during the pandemic. Moreover, we used a relatively large, heterogeneous sample and a comprehensive, multidimensional intelligence test. We were able to compare the results of our sample with two highly similar prepandemic samples using propensity score matching. Last, we retested a large portion of the sample to longitudinally investigate the development of intelligence during the pandemic.

However, the present study also has several limitations that restrict the interpretation of the results. First, due to the pandemic affecting all students, we were not able to use a control group but had to rely on samples collected in previous years. Cohort effects cannot be completely excluded, although we tried to minimize their influence through propensity score matching and the use of two different prepandemic comparison groups. We could not control for potential differences in socioeconomic status (SES) between the samples because no equivalent measure was used in all three cohorts. It would have been beneficial to control for SES because of its influence on cognitive development and on the bidirectional relationship of intelligence and academic achievement [ 9 ]. SES differences between samples therefore may account for some of the observed test score differences. However, large differences in SES between the samples are unlikely because the 2012 and 2020 samples were drawn from the same four schools. Regarding the impact of SES on the longitudinal change during the pandemic in the 2020 sample, we did not have a comprehensive SES measure available. However, we had information on the highest level of education of parents. When adding this variable as a predictor in the LCA analyses, the results did not change, and parents’ education was no significant predictor of change.

Second, both measurement points of the study fell within the pandemic. A prepandemic measurement is not available for our 2020 sample. This limits the interpretation of the change in test scores over the course of the pandemic, even though we compared the observed retest effects with those found in meta-analysis and a previous retest-study of the BIS-HB.

Third, the 2020 measurement occurred only a few weeks after the summer break. It has often been shown that the summer break causes a decrease in math achievement test scores [ 78 ] as well as intelligence test scores [ 79 ]. However, this “summer slide” effect on intelligence seems to be very modest in size [ 80 ] and is therefore unlikely to be fully responsible for the large observed cohort differences in the present investigation.

Fourth, perceived stress was only measured by a short, retrospective scale. The resulting scores may not very accurately represent the actual stress levels of the students over the school year. Moreover, perceived stress was not measured at the first measurement point, so changes in stress levels during the pandemic could not be examined. This limits the interpretation of the absence of stress effects on changes in intelligence.

Fifth, the matched groups in Analysis 1b were somewhat unbalanced with regard to grade level ( Table 1 ). The students in the 2020 sample tended to be in higher grades while being the same age. However, this pattern is unlikely to explain the differences in intelligence. The students in the 2020 sample tended to have experienced more schooling at the same age than the other samples, which would be expected to be beneficial for intelligence development [ 10 , 11 ].

Sixth, there was some attrition between the first and second measurement of the 2020 sample. This was due to students changing schools or school classes, being sick or otherwise absent on the second day of testing or failing to provide parental consent for the second testing. It may be plausible that especially students with negative motivational or intellectual development changed school or avoided the second testing. This means that the improvement between the first and second time of measurement may be somewhat overestimated in the present analyses.

Seventh and last, only a modest percentage of the samples were matched in the PSM procedure because we followed a conservative recommendation for the caliper size [ 55 ] that yielded a very balanced matching solution. The limited common support somewhat diminishes the generalizability of the findings to the full samples.

Implications

The pandemic and the associated countermeasures affected the academic development of an entire generation of students around the world, as evidenced by decreases in academic achievement [ 3 ]. Simulations predict a total learning loss between .3 and 1.1 school years, a loss valued at approximately $10 trillion [ 81 ]. Although we cannot make any causal claims with the present study, our results suggest that these problems might extend to students’ intelligence development. They point out that possible detrimental effects especially took place during the first months of the pandemic. Moreover, our longitudinal results do not point to any recovery effects.

As schooling has a positive impact on students’ cognitive development, educational institutions worldwide have a chance to compensate for such negative effects in the long term. As interventions aimed at the improvement of academic achievement also affect intelligence, [ 9 ] the decline in intelligence could be recovered if targeted efforts are made to compensate for the deficit in academic achievement that has occurred. Furthermore, schools could pay attention to offering intellectually challenging lessons or supplementary programs in the afternoons or during vacations, as intellectually more stimulating environments have a positive effect on intelligence development [ 82 ].

A second implication concerns current intelligence testing practice. If there is a general, substantial decrease in intelligence test performance, testing with prepandemic norms will lead to an underestimation of the percentile rank (and thus IQ) of the person being tested. This can have significant consequences. For example, some giftedness programs use IQ cutoffs to determine eligibility. Fewer students tested during (or after) the pandemic may meet such a criterion. If the lower test performance persists even after the pandemic, it may even be necessary to update intelligence test norms to account for this effect.

As discussed in the previous section, the present study has several limitations. The results can therefore only be regarded as a first indication that the pandemic is affecting intelligence test performance. There is a need for further research on this topic to corroborate the findings. It is obviously no longer possible to start a longitudinal project with prepandemic measurement points. However, the present article presented a way to investigate the effect of the pandemic if prepandemic comparison samples are available. Ideally, the prepandemic samples would have been assessed shortly before the pandemic onset to minimize differences between cohorts due to the (reverse) Flynn effect, changes in school curricula, or school policy changes. If a sample was assessed very recently before the pandemic, it may also be possible to retest the participants for the investigation of the pandemic effects. Although we cannot make any causal claims with the present study, our results suggest that COVID-19-related problems might extend to students’ cognitive abilities. As intelligence plays a central role in many areas of life, it would be important to further investigate differences between prepandemic and current student samples to account for these differences in test norms and for possible disadvantages by offering specific interventions.

Funding Statement

The study was supported by a grant awarded to M.B. by the Research Fund of the University of Trier (FoF/A 2020-02). https://www.uni-trier.de/forschung/nationale-forschungsfoerderung/inneruniversitaere-forschungsfoerderung-nikolaus-koch-stiftung/forschungsfonds The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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• Published: 12 June 2017

Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing

• Andrew J. K. Phillips 1 , 2   na1 ,
• William M. Clerx 1 , 2   na1 ,
• Conor S. O’Brien 1 ,
• Akane Sano 3 ,
• Laura K. Barger 1 , 2 ,
• Rosalind W. Picard 3 ,
• Steven W. Lockley 1 , 2 ,
• Elizabeth B. Klerman 1 , 2 &
• Charles A. Czeisler 1 , 2  

Scientific Reports volume  7 , Article number:  3216 ( 2017 ) Cite this article

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• Human behaviour
• Neurophysiology

The association of irregular sleep schedules with circadian timing and academic performance has not been systematically examined. We studied 61 undergraduates for 30 days using sleep diaries, and quantified sleep regularity using a novel metric, the sleep regularity index (SRI). In the most and least regular quintiles, circadian phase and light exposure were assessed using salivary dim-light melatonin onset (DLMO) and wrist-worn photometry, respectively. DLMO occurred later (00:08 ± 1:54 vs. 21:32 ± 1:48; p < 0.003); the daily sleep propensity rhythm peaked later (06:33 ± 0:19 vs. 04:45 ± 0:11; p < 0.005); and light rhythms had lower amplitude (102 ± 19 lux vs. 179 ± 29 lux; p < 0.005) in Irregular compared to Regular sleepers. A mathematical model of the circadian pacemaker and its response to light was used to demonstrate that Irregular vs. Regular group differences in circadian timing were likely primarily due to their different patterns of light exposure. A positive correlation (r = 0.37; p < 0.004) between academic performance and SRI was observed. These findings show that irregular sleep and light exposure patterns in college students are associated with delayed circadian rhythms and lower academic performance. Moreover, the modeling results reveal that light-based interventions may be therapeutically effective in improving sleep regularity in this population.

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Introduction.

The sleep of college students is often variable in both duration and timing, with many students suffering from considerable sleep deficiency 1 , 2 , 3 , 4 , 5 . In adults, short sleep duration has been associated with cognitive impairments, including increased reaction time and reduced cognitive throughput 6 ; motor vehicle accidents and early mortality 7 ; elevated risk for metabolic disorders, including obesity 8 , type 2 diabetes 9 , and cardiovascular disease 10 ; and psychiatric disorders 11 . Sleep is multidimensional, however, and its importance to health and performance may not be purely dependent upon its daily duration. The composition of sleep varies depending on circadian phase and the time of day at which sleep occurs 12 , 13 . Circadian phase is affected by light exposure; even room light shifts circadian phase significantly in humans 14 . Individuals who frequently change their sleep timing, and consequently their pattern of light/dark exposure, may experience misalignment between the circadian system and the sleep/wake cycle, since the circadian clock takes time to adjust to schedule changes 15 . Such misalignment may have an adverse effect on both cognitive function and health 7 , 16 .

To date, researchers have analyzed variability in measures associated with nighttime sleep episodes, such as total nighttime sleep, midpoint of the nighttime sleep episode, nighttime sleep onset, or morning awakening time 1 , 3 , 17 , 18 , 19 , 20 , including two recent studies that correlated variance in these measures with weight gain 21 and poor academic performance 22 . Variables based on the timing of nighttime sleep episodes may be difficult to generalize to individuals with extremely irregular sleep, polyphasic sleep, or rotating schedules, because these individuals often have no identifiable nighttime sleep episode, many daytime sleep episodes, or nights with no sleep (all-nighters). A measure of inter-daily stability has been proposed for quantifying regularity in activity measures 23 , but this metric quantifies overall variability in a time-signal after averaging across days, rather than quantifying how rapidly sleep patterns change between consecutive days. When considering the biological impact of irregular sleep, rapid changes in sleep timing are important to quantify, because they are particularly challenging for the circadian system to accommodate. Chronic jet-lag induced by constantly shifting schedules increases mortality 24 and tumor growth rate 25 in mice, while rotating night shift work is associated with increased risk of heart disease 26  and breast cancer in humans 27 . One previous study of college students collected data from regular sleepers, defined as individuals who habitually slept from midnight to 08:00 for 7–8 h, and irregular sleepers, defined as individuals whose sleep/wake times varied by “about 2–4 h” 18 . That study found that regular sleepers have better mood and psychomotor performance, and increased time in REM and slow-wave sleep.

Motivated by our interest in capturing changes in sleep timing on a day-to-day (circadian) timescale, we constructed a novel metric for sleep regularity, called the Sleep Regularity Index (SRI). This index calculates the percentage probability of an individual being in the same state (asleep vs. awake) at any two time-points 24 h apart, averaged across the study. The index is scaled so that an individual who sleeps and wakes at exactly the same times each day scores 100, whereas an individual who sleeps and wakes at random scores 0. This index is constructed on the reasoning that changes in sleep schedules from one 24-h interval to the next may cause circadian disruption and thus impact normal biological functioning and health. The SRI differs from previous approaches in that it does not require designation of a main daily sleep episode, and can thus be applied in populations such as college students, where additional daytime sleep episodes and all-nighters are commonly observed.

Using the SRI, we assessed real-world sleep patterns in college undergraduates and classified individuals as Regular (top quintile) or Irregular (bottom quintile). We examined the relationships among SRI, sleep duration, distribution of sleep across the day, and academic performance during one semester. In addition, we measured the phase of the endogenous circadian melatonin rhythm and light exposure patterns in participants classified as Regular or Irregular. Differences in circadian timing of endogenous melatonin secretion and sleep propensity between Regular and Irregular sleepers could potentially be due to systematic differences in circadian physiology. For example, Irregular sleepers could have longer intrinsic circadian periods, leading to delayed circadian rhythms 28 and increased overlap of sleep with morning classes, leading to irregular sleep schedules. Alternatively, the difference in circadian timing could be due to different patterns of light exposure associated with Regular vs. Irregular sleepers, because light exposure during the early biological night delays the circadian clock 29 . We tested these mechanistic hypotheses using a previously-validated mathematical model of the human circadian clock and its response to light.

Sleep regularity is independent of sleep duration

Individual sleep patterns across the 30 days ranged from highly irregular to highly regular (SRI range: 38–86, mean ± std = 73 ± 11). The distribution of SRI was negatively skewed and non-normal (p < 0.02, Kolmogorov-Smirnov). Daily average sleep duration ranged from 5.7–9.3 h (mean ± std = 7.4 ± 0.7 h) and followed a normal distribution (p = 0.96, Kolmogorov-Smirnov). Examples of individual sleep patterns are shown in Fig.  1 . In this population of students living under real-world academic and social constraints, there was no correlation between average daily sleep duration and SRI (r = 0.05, p = 0.71).

Two dimensions of sleep: duration and regularity. ( A ) Average daily sleep duration vs. sleep regularity index (SRI) for all participants ( n  = 61) assessed across the whole study interval. Participants identified for the Irregular ( n  = 12) and Regular ( n  = 12) groups at the study mid-point are red circles and blue squares, respectively. Other individuals are white triangles. As explained in the Methods, the SRI values differed slightly by end of study, so those identified as most extreme at the study midpoint did not necessarily remain most extreme at end of study; however, the differences between Regular and Irregular groups remained highly significant (see Results). Error bars indicate mean and standard deviation for Regular and Irregular groups in both SRI and sleep duration. Sleep patterns for four participants collected using daily diaries are shown using double-plotted raster diagrams, where black bars indicate episodes of sleep and gray bars indicate missing data. Four examples are displayed: ( B ) an Irregular long sleeper, ( C ) an Irregular short sleeper, ( D ) a Regular long sleeper, and ( E ) a Regular short sleeper.

At the study midpoint (using data from days 1–14), we identified the 12 individuals in the lower quintile (SRI range: 35–64, mean ± std = 52 ± 10, the “Irregular” sleepers) and the 12 individuals in the upper quintile (SRI range: 81–87, mean ± std = 84 ± 2, the “Regular” sleepers). We note that any measure of sleep regularity will require more data to reliably estimate for an irregular sleeper than for a regular sleeper, and no measure of sleep regularity can be perfectly estimated from a finite interval of study. As can be seen in Fig.  1 , there are therefore participants who would have qualified for the Regular or Irregular groups based on the full 30 days, but were not selected at study midpoint. Nevertheless, our Irregular and Regular groups remained strongly separated by SRI, indicating a stable difference between these extremes. At the end of the 30 days, the SRI of the Irregular (range: 38–69, mean ± std = 56 ± 10) and Regular (range: 78–87, mean ± std = 83 ± 3) groups remained significantly different (p < 10 −4 , rank-sum test).

There was no significant difference in average daily sleep duration between the Irregular (7.16 ± 0.64 h) and Regular (7.27 ± 0.59 h) groups (p = 0.68, t-test). On baseline questionnaires, Irregular sleepers reported, relative to Regular sleepers, poorer sleep quality on the Pittsburgh Sleep Quality Index (6.83 ± 2.39 vs. 3.75 ± 2.39; p < 0.01), later mid-sleep time on free days (7:05 ± 1:23 vs. 4:53 ± 0:56; p < 0.001), and later average diurnal preference (more ‘evening-type’) on the Morningness-Eveningness Questionnaire (40.1 ± 6.2 vs. 54.3 ± 10.2; p < 0.001). We did not find any significant difference in the sex distribution of our groups: the Regular group had 6 M 6 F, while the Irregular group had 7 M 5 F.

Irregular sleepers have delayed sleep timing and more daytime sleep

As a group, Regular sleepers expressed a robust daily rhythm in the percentage of time they spent asleep averaged across the day in 1-h time bins (Fig.  2A ). As summarized in Table  1 , Regular sleepers obtained significantly more sleep during the clock night (defined as 22:00 to 10:00) and significantly less sleep during the clock day (defined as 10:00 to 22:00) than Irregular sleepers. Regular sleepers were asleep 55% of the clock night and only 1% of the clock day. By contrast, Irregular sleepers were asleep for 42% of the clock night and 11% of the clock day.

Sleep/wake and light/dark cycles differ between Regular and Irregular groups. Gray lines show individual data ( n  = 12 for each sleep/wake panel and n  = 11 for each light/dark panel). Colored lines show group mean and standard deviation in one-hour bins, with data for each individual averaged across the whole study interval (i.e., multiple days). Dark gray bars indicate clock night (22:00 to 10:00). Left panels: Sleep/wake rhythm (percentage of time asleep) for ( A ) Regular and ( B ) Irregular sleepers. Right panels: Normalized light levels for ( C ) Regular and ( D ) Irregular sleepers.

As expected, Irregular sleepers (Fig. 2B ) averaged more daytime sleep episodes (naps) per week than Regular sleepers (3.02 ± 1.47 vs. 0.75 ± 0.80; p < 0.002, rank-sum test) and obtained more daytime compensatory sleep per week (5.35 ± 2.82 h vs. 0.72 ± 0.65 h; p < 0.0005, rank-sum test). The fitted peak of the sleep propensity rhythm (i.e., the daily rhythm in percentage likelihood of being asleep) was significantly later for the Irregular group; 95% confidence intervals for the time of the peak for the first harmonic of a two-harmonic fit were 06:33 ± 0:19 in the Irregular sleepers vs. 04:45 ± 0:11 in the Regular sleepers (p < 0.005).

Sleep onset and morning awakening times differed significantly between groups. In Irregular sleepers, the average subjectively-reported time of sleep onset was 03:02 ± 1:23 vs. 01:15 ± 0:51 in Regular sleepers (p < 0.001, rank-sum test). In Irregular sleepers, the average time of morning awakening was 10:00 ± 1:41 vs. 08:27 ± 0:51 in Regular sleepers (p < 0.03, rank-sum test).

Interestingly, the Irregular and Regular groups, which were defined using SRI, did not always significantly differ by other commonly-used metrics of sleep variability. Standard deviations of sleep onset times (2.05 ± 0.72 h vs. 1.20 ± 0.16 h; p < 0.001, rank-sum test) and wake times (2.08 ± 0.99 h vs. 1.10 ± 0.48 h; p < 0.01, rank-sum test) were significantly different between Irregular and Regular groups. However, the standard deviation of mid-sleep time (1.64 ± 0.64 h vs. 0.99 ± 0.26 h; p = 0.053, rank-sum test) did not significantly differ between Irregular and Regular groups.

Irregular sleepers have a lower amplitude light rhythm

Irregular sleepers received different patterns of light exposure (Fig.  2C and D ), with summary metrics in Table  1 . The amplitude of the daily light/dark cycle (light rhythm) was lower in Irregular sleepers, reflecting a smaller difference between day-time and night-time illuminance. 95% confidence intervals for the first-harmonic amplitude of a two-harmonic fit were 102 ± 19 lux (Irregular) vs. 179 ± 29 lux (Regular; p < 0.005). Irregular sleepers received significantly less day-time light (Table  1 ) and had a broader range of light-exposure centroid times (9.6 h vs. 4.5 h). On average, light centroid times were later in the Irregular sleepers than in Regular sleepers, although this difference was not significant (14:18 ± 2:37 vs. 13:05 ± 1:19; p = 0.18, rank-sum test). When light levels were normalized on an individual basis, by dividing by that individual’s average daily illuminance, Irregular sleepers were found to receive relatively more light during the biological night (DLMO to 10 h post-DLMO) and clock night (Table  1 ). Although the light pattern appeared slightly delayed in the Irregular group, phase parameters for the two-harmonic fits were not significantly different between groups. We note that parametric fits are not ideal for quantifying the effects of the light pattern on the circadian pacemaker, since the sensitivity of the circadian pacemaker to light varies across the day and with previous light exposure history. This point is addressed below by our use of a mathematical model to explicitly predict an individual’s circadian phase of entrainment from the individual light patterns.

Irregular sleepers have delayed onset of melatonin secretion, which is predicted by their patterns of light exposure

On average, Irregular sleepers had significantly later DLMO (00:08 ± 1:54 vs. 21:32 ± 1:48, p < 0.003) (Fig.  3A–C ). This group difference remained significant even when the earliest individual in the Regular group was removed (p < 0.005). When light exposure patterns were given as inputs to a mathematical model of the human circadian pacemaker, with all other model parameters fixed at default values (i.e., assuming no inter-group differences in circadian physiology), the model predicted an average 1.7 h delay (p < 0.01, t-test) in DLMO timing in the Irregular group compared to the Regular group, whereas the actual average delay of the Irregular group compared to the Regular group was 2.2 h. An example of model inputs and outputs is shown in Fig.  4 . On an individual basis, predictions were less accurate (11 Regular and 10 Irregular individuals had viable light data for modeling). In the Regular group, 4 of 11 predictions were within ± 1 h of the observed DLMO timing, and 9 of 11 were within ± 2 h. In the Irregular group, 5 of 10 predictions were within ± 1 h, and 8 of 10 were within ± 2 h. Linear fits to Regular (r = 0.50, slope = 0.31) and Irregular (r = 0.34, slope = 0.26) groups both had slopes less than 1, implying the model predicted less inter-individual variability in DLMO timing within each group than existed in the data. Within the participants in whom we assessed DLMO, we found that SRI and DLMO were negatively correlated (r = −0.66, p < 0.001, Fig.  5B ).

Melatonin secretion is delayed in Irregular sleepers. Top panel: ( A ) Timing of salivary dim light melatonin onset (DLMO) for Regular ( n  = 12) and Irregular ( n  = 12) groups. Individuals in each group are shown as dots, with y-axis position jittered for visibility. Groups means (triangles) and standard deviations (error bars) are shown. Middle two panels: Time course for salivary melatonin concentration are shown for Regular participants in ( B ) and Irregular participants in ( C ), along with average sleep midpoint times for the nighttime sleep block for each individual (black dots), with y-axis position jittered for visibility. Gray lines show individuals in 1-h bins. Colored lines with error bars show group mean and standard deviation in 1-h bins. Bottom panel: ( D ) Actual timing of DLMO vs. model prediction for timing of DLMO, assessed using saliva. Data points correspond to individuals in the Regular (blue square) and Irregular (red circle) groups. Error bars show mean and standard deviation for each group. Differences in group averages are displayed. The dashed lines show linear regressions for each group.

Example of model inputs and outputs for one participant. Variables are shown for days 280–300 of a 300-day simulation for one participant from the Regular group. ( A ) Binned light levels in lux. ( B ) The two circadian pacemaker variables, x (blue) and x c (red). ( C ) The predicted salivary melatonin concentration. ( D ) The clock-time of Dim Light Salivary Melatonin Onset (DLSMO) on each day.

Correlations between sleep regularity index (SRI), grade point average (GPA), and timing of melatonin secretion. Panels (A, B and C) show the relationships between the variables: SRI, GPA, and salivary DLMO. Dashed lines show the linear fits, with r-values and p-values shown for each linear (Pearson) correlation. Each data point represents an individual, with colors indicating whether the individual was a member of the Regular (blue), Irregular (red), or neither group (black). Note that DLMO was only assessed in the Irregular and Regular participants.

Sleep regularity is positively associated with academic performance

SRI had a positive linear correlation with Grade Point Average (GPA) in the whole sample ( n  = 59, Pearson r = 0.37, p < 0.004; Fig.  5A ). An increase of 10 in SRI was associated with an average increase of 0.10 in GPA. For reference, median GPA at Harvard has been recently estimated 30 as 3.64, with a maximum possible 4.00. As secondary analysis, we calculated GPAs for the initially designated Irregular (3.41 ± 0.33) and Regular (3.60 ± 0.32) groups; the difference in these subgroups ( n  = 12 each) was not significant (p = 0.16). When Irregular and Regular groups were designated using the full 30-day sleep record, rather than 14 days, there was a significant difference in GPA (p < 0.02) between Irregular (3.42 ± 0.34) and Regular (3.72 ± 0.24) groups. There was no significant linear correlation between sleep duration and SRI (r = 0.13, p = 0.29) or sleep duration and GPA (r = 0.12, p = 0.37). DLMO was also not significantly correlated with GPA (r = 0.37, p = 0.08). Since sleep timing was found to be associated with SRI, we tested whether the pre-study score on the Morningness-Eveningness Questionnaire was predictive of GPA, but found no significant correlation (r = −0.01, p = 0.96).

To determine whether our results were dependent upon our choice of regularity metric, we tested relationships between SRI, GPA, and previously used metrics for sleep regularity that are based only on nighttime sleep. SRI had highly significant negative linear (Pearson) correlations with standard deviations of sleep onset time (r = −0.66, p < 10 −8 ), wake time (r = −0.62, p < 10 −7 ), and mid-sleep time (r = −0.62, p < 10 −7 ). We also found negative linear correlations between GPA and standard deviations of sleep onset time (r = −0.43, p < 0.001), wake time (r = −0.29, p = 0.02), and mid-sleep time (r = −0.31, p < 0.02). These results collectively demonstrate a robust positive relationship between sleep regularity and academic grades. We emphasize, however, that this is an association, and we cannot determine causality.

Our findings demonstrate that irregular sleep schedules in a specific population of college students are associated with a significant circadian phase delay in the timing of both the endogenous melatonin rhythm and in the sleep propensity rhythm—equivalent to traveling two to three time zones westward—compared to students on a more regular sleep/wake schedule. We also found that sleep regularity is positively correlated with academic performance. Sleep regularity was uncorrelated with sleep duration, suggesting that regularity captures another informative dimension of sleep. The SRI metric we used here captures a specific type of regularity–day-to-day differences in sleep patterns–and does not require a main daily sleep episode to be designated, which is advantageous in populations with highly irregular sleep patterns. We note that this metric may be complemetary to another metric recently devised to capture day-to-day changes in timing of the main daily sleep episode 31 .

Our findings are consistent with a previous study 32 that found later wake times are associated with worse grades in first-year college students. Our results suggest this association may be mediated by sleep regularity. This interpretation is consistent with an earlier study that identified irregular sleep as a risk factor for worse academic performance in medical students 33 . We anticipated that Irregular sleepers might face decreased sleep opportunities due to conflicts between their delayed, erratic schedules and classes. Instead, we found that Irregular sleepers had the same total sleep as Regular sleepers. They achieved this by sleeping more during the daytime. This suggests that homeostatic control of sleep functions similarly in both groups, forcing Irregular sleepers to have compensatory daytime sleep episodes when they obtain insufficient nighttime sleep, although we do not have measures of sleep intensity by which we could quantify the dynamics of sleep homeostasis. This pattern of sleep is similar to blind individuals with non-24-hour sleep/wake disorder–they maintain the same total sleep duration via compensatory daytime sleep episodes, even when their sleep is highly fragmented due to their sleep/wake cycle being out-of-sync with their circadian cycle 34 .

Differences in academic performance were not associated with average sleep duration in our population, and our data suggest that polyphasic sleep schedules that distribute sleep around the clock may be less effective for students, even if they maintain total sleep time. We note, however, that we cannot establish a causal relationship between sleep patterns and academic performance; sleep regularity may indeed be a proxy for regularity in other aspects of daily activity and schedules. The ability to sleep during the daytime as a compensation strategy after insufficient nocturnal sleep may also be specific to undergraduate students living on campus. This strategy would not be available to most adults who work full time or to students who do not live near campus. In populations that are limited in their ability to nap, sleep duration may positively correlate with SRI.

The association between SRI and circadian timing has at least two competing mechanistic hypotheses. One hypothesis is that Regular and Irregular sleepers differ in their circadian physiology. For example, Irregular sleepers could have longer intrinsic circadian periods 35 . Under this hypothesis, irregular sleep schedules would be a consequence of delayed circadian rhythms, which would promote later sleep onset and conflict with early class schedules, leading to irregular sleep patterns.

A countervailing hypothesis is that Regular and Irregular sleepers do not differ in their circadian physiology. Under this (null) hypothesis, differences in circadian timing would be a consequence of irregular sleep schedules and their associated light patterns. Using a mathematical model of human circadian rhythms, we conclude that the results are primarily consistent with the latter (null) hypothesis. While there may be differences in circadian physiology between the groups, which may also account for individual variability that the model fails to capture, these are not the primary reason for the group differences. We therefore conclude that Irregular sleepers have later circadian timing predominantly due to the characteristics of their light profiles: these can be summarized as lower-amplitude daytime light exposure, together with a relatively greater ratio of nighttime light exposure to daytime light exposure. Increased exposure to daytime light desensitizes the circadian clock to the effects of light at nighttime 36 , which may help protect Regular sleepers from the delaying effects of exposure to electronic light-emitting devices in the early biological night 37 , 38 . Moreover, insufficient exposure to light in the early biological day would be predicted to reduce the amount of corrective phase advance in Irregular sleepers 39 .

A potential limitation in our ability to predict DLMO timing at the individual level is the fact that melatonin was assessed on only one day. It is not well understood how stable DLMO timings are in a college student population, but one would expect variable light patterns to cause some shifting from day to day, as is predicted by our model. Individuals with irregular sleep or light patterns in particular may have less stable melatonin phases, which could account for a data vs. model mismatch. This is consistent with our observation that the correlation between data and model was weaker in the Irregular group than in the Regular group.

Light studies in humans and other animals have demonstrated that the intrinsic period of the circadian pacemaker is dependent on prior light exposure 40 , 41 . A light pulse that delays the circadian rhythm also usually causes a transient lengthening of the circadian period, which may last for weeks 42 . This plasticity is potentially germane to our results, since a longer circadian period theoretically implies a later phase angle of entrainment, given the same light pattern and the same sensitivity of the circadian system to light 43 . Recently, it was reported that individuals with delayed sleep phase disorder have unusually long intrinsic circadian periods, as measured under an ultradian forced desynchrony protocol 44 . It is therefore plausible that late or irregular schedules could induce long-lasting changes to the circadian clock, including lengthening of the intrinsic circadian period, which would further encourage late or irregular schedules.

Why certain individuals develop irregular sleep is an important question not directly addressed by our study. Individuals who are biologically predisposed to later schedules may find this amplified by use of inappropriately timed light. This hypothesis is supported by a recent study that found large inter-individual differences in circadian timing vanished when individuals were exposed to only natural outdoor light 45 . Other studies have linked eveningness with lower self-control 46 , behavioral/emotional problems in adolescents 47 , and depressive symptoms 48 , 49 . These factors may interact. For example, lower self-control may reduce efforts to maintain a stable bed-time or reduce use of electric light at night, while depression may decrease motivation to maintain a regular morning schedule or obtain regular physical activity, decreasing daytime light. Sex differences may also contribute to differences in sleep timing 35 , 50 , but our experiment was not powered to test for interactions between sex, sleep timing, and regularity. We also did not attempt to control for phase in the menstrual cycle.

Our findings could potentially be used to design and test interventions. Delayed circadian rhythms and irregular sleep patterns are associated with weight gain 21 and poor academic grades 22 . Although further experiments are needed to identify factors that predispose individuals to adopting irregular sleep, our results suggest this could be treated in undergraduates through light interventions used to advance circadian rhythms, and education about importance of regular sleep schedules. Adoption of a stable sleep pattern would regulate light/dark cycles, reinforcing regular behavior. Recent empirical findings suggest that effective light interventions could easily be developed at low user burden 51 . Notably, one study that experimentally enforced regular sleep schedules for 38 days in college students with habitually irregular sleep patterns found no change in time spent in each sleep stage, auditory vigilance, addition test performance, or mood between conditions 52 . However, that 1982 study was underpowered ( n  = 12) and confounded by many factors, including pooling of data from an unsuccessful pharmaceutical trial, such that benzodiazepines were administered each night to half ( n  = 6) of the participants; inconsistent timing of both sleep and performance testing between participants; and self-administration of performance tests under uncontrolled conditions. Indeed, a more recent study in college undergraduates found that experimentally enforcing regular schedules for 28 days, with a minimum of 7.5 h daily sleep, improved subjective alertness compared to schedules with the same minimum sleep duration but no requirements on sleep regularity 17 . In light of the findings from our study, the question of whether imposition of a regular schedule can improve sleep, health, and performance should be revisited in an experimental design.

In summary, our results demonstrate that irregular sleep is associated with delayed circadian timing, and that most of this delayed timing can be explained, using a mathematical model, by the differences in patterns of light exposure. This is important, because it suggests a feedback loop between an individual’s sleep regularity, their sleep timing, and their light exposure pattern. Individuals who adopt irregular sleep patterns are subject to a light pattern that encourages circadian delay and thus may lead to reinforcement of delayed and irregular sleep patterns. This suggests that light-based interventions may be successful in treating irregular sleep in this population. While the data here cannot be used to make causal inferences regarding the relationships between sleep regularity, circadian timing, and academic performance, our findings nevertheless highlight sleep regularity as a potentially important and modifiable factor – independent from sleep duration – in determining academic performance and circadian timing.

Participants

Full-time undergraduates (excluding first-years), aged 18+, were recruited from Harvard College. Enrollment was not based on class schedule or types of classes. Participants were excluded if pregnant or traveling >1 time-zone one week before or during study. 63 participants were enrolled. Two discontinued in the first week for personal reasons. Remaining participants (32 M 29 F) were aged 18–24 (20.23 ± 1.27). One participant selected for the Irregular group discontinued for personal reasons. The next eligible participant was invited as replacement and successfully completed the study.

Study approval

All participants provided written informed consent. Research was approved by the Partners Health Care Human Research Committee and the Committee on the Use of Human Subjects at Harvard University, and was in compliance with HIPAA regulations and the Declaration of Helsinki. The study did not meet the criteria for a ClinicalTrials.gov registration.

Participants lived in campus housing and reported their self-selected sleep/wake schedule for ~30 days (26–36 days, mean 30.67 days) by online diary during Fall 2013. After consent, participants completed the Pittsburgh Sleep Quality Index 53 and Horne-Östberg Morningness-Eveningness Questionnaire 54 . Individuals self-reported their current GPA after study. On day 15, we classified participants using SRI. The highest ( n  = 12) and lowest ( n  = 12) quintiles were selected to wear actiwatches and complete dim-light saliva collection. Participants were blinded to group (i.e., they did not know that they had been assigned to the most regular or most irregular group, only that they had been selected for further study). Diaries were based on ones previously validated against the gold-standard for sleep assessment (polysomnography) in resident physicians 55 and used in shift-workers 56 . Participants completed diaries shortly after awakening to report time of sleep onset, morning awakening, and the timing and duration of any awakenings during their main daily sleep episode. Participants also reported the timing and duration of any other sleep episodes (naps) and any actiwatch removals. To ensure accuracy of entries, each diary was accessible for only 24 h to prevent post-hoc completion; reminders were sent at 08:00. In addition, online diaries were checked daily by study staff, and participants with errors or missing fields were contacted within 24 h to encourage completion and clarify any errors. Using this highly-supervised approach, sleep/wake state was provided for 95% of the study across all individuals; individual completion rates ranged from 72–100%. For day 15 classification, data from days 1–14 were used. Only pairs of non-missing time-points were used to compute SRI. For final analysis, SRI was computed using the longest interval of a whole number of weeks with no missing data to avoid day-of-week bias (i.e., a time interval that is a multiple of 7 days). 2 individuals had one week, 7 had two weeks, 7 had three weeks, and 43 had four weeks. We note that the interval of sleep diary collection overlapped a daylight savings time transition (November). Differences in SRI calculated with a universal time or a daylight-savings-corrected time were negligible, so this did not affect group selection for the later light and melatonin collection.

Light data were obtained minute-by-minute from 22 of 24 participants (11 Regular and 11 Irregular) using Motionlogger-L (Ambulatory Monitoring, Inc., Ardsley, NY) for approximately one week after the study midpoint. For one participant, the device malfunctioned. Another failed to return the watch. All actiwatches were tested against a calibrated light meter for 5–10 min at ~1–5 lux and levels ranging ~90–3000 lux. At 1–5 lux, all actiwatches were within 5 lux of the light meter. From 90–3000 lux, all watches were within 0.25 log 10 -units of the light meter. Saliva samples were collected hourly from 7 h prior to 3 h after habitual bedtime on a Friday night at the end of the study. There were no restrictions on prior sleep or class attendance. Participants were permitted to take brief naps between samples, interact with others, and use non-light emitting entertainment (e.g., books, board games, music). Participants began ≤5 lux conditions at least 45 minutes before first sample. Participants were instructed to avoid consuming foods and beverages that might impact melatonin levels, i.e., citrus fruits, bananas, and milk. They were also instructed to avoid eating or drinking, and to maintain stable posture, for the 20 min prior to each sample. 13 participants completed saliva collection in a central on-campus room supervised by investigators. The other 11 completed the protocol in individual on-campus rooms. An investigator visited rooms to ensure appropriate dim-light conditions, and light levels were confirmed using the actiwatches. All participants were additionally provided filtered goggles, in case of unanticipated light exposure, and red nightlights for rooms where light was necessary (e.g., bathrooms) to minimize melatonin suppression 57 . Samples were frozen upon collection. This procedure for assessing circadian phase in the field is based on a validated protocol with a field success rate of 62.5% compared to in-lab DLMO 58 . Salivary melatonin concentration was determined by standard RIA with analytical sensitivity of 0.2 pg/mL and an intra-assay %CV of 10.8% at 2.1 pg/mL and 11.4% at 20.4 pg/mL (BÜHLMANN Direct Saliva Melatonin RIA, Schönenbuch, Switzerland). DLMO time was determined by interpolating points immediately above and below a threshold of 5 pg/mL. In one participant, the first assay of 5.76 pg/mL was slightly above threshold, with values then rising. To compute DLMO we could extrapolate back to the estimated time of 5.00 pg/mL (19:25) or take the first assay time as DLMO (19:42). Since the individual was in the Regular (earlier) group, we used the latter assumption to be conservative.

Statistical comparisons between groups were computed using the non-parametric Wilcoxon rank-sum test, unless there was strong evidence of a normal distribution, in which case a two-tailed t-test was used. The average number of naps per week and amount of time spent napping was computed using the longest interval of a whole number of weeks with no missing data. For participants in Regular and Irregular groups, average clock time of sleep onset, midsleep, and wake time were calculated using vector averages. These timings were also computed by traditional means (linear averaging) for comparisons with SRI. The centroid of light exposure time was calculated using a weighted vector average, after averaging illuminances in 1-h bins with respect to clock time, and weighting each bin in proportion to its average illuminance. Grade Point Average (GPA) was reported by 59 participants. The two non-reporters were neither Regular nor Irregular. As primary analysis, we computed the correlation between SRI and GPA. As secondary analysis, we tested for significant differences in GPA between Regular and Irregular groups.

Sleep and light data were averaged in 1-h time bins, using a weighted average for each individual with equal representation for each of day of the week. To determine amplitude and phase of sleep and light rhythms, two-harmonic fits were performed using the least-squares estimator nlinfit in Matlab. Confidence intervals for model parameters were computed using nlparci. Night was defined in two ways: clock time (22:00 to 10:00) and biological time (DLMO to 10 h post-DLMO, based on typical timing of melatonin release 59 ). Only data from before dim-light salivary melatonin assessment were analyzed. Intervals of usable light data ranged 89–335 h (mean of 171 h). SRI was computed as the likelihood that any two time-points (minute-by-minute) 24 h apart were the same sleep/wake state, across all days. The value could theoretically range 0 to 1, and was rescaled ( y  = 200 ( x  − 1/2)) to give a range of −100 to 100. This rescaling was chosen to give a more intuitive range. In practice, individuals will only display sleep patterns that range between an SRI of 0 (random) and 100 (periodic). Values less than 0 are still theoretically possible (e.g., sleep for 24 h, wake for 24 h, etc.), but very unlikely to be observed.

A previously validated model of the human circadian pacemaker, its sensitivity to light, and salivary melatonin concentration 60 was used to predict circadian phase. This model has three components: (i) A model of how light is processed by the retina and conveyed as a signal to the central circadian pacemaker. (ii) A limit-cycle oscillator that describes the dynamics of the pacemaker and the phase/amplitude-modifying effects of light. (iii) A multi-compartment model of melatonin release from the pineal gland, diffusion into and elimination from plasma, and diffusion into saliva. Melatonin release from the pineal gland begins and ends at certain circadian phases, with the timing of DLMO being sensitive to the timing at which this begins. In prior work, the time of release was a free parameter, specified in terms of clock time. Here, release time was fixed in terms of oscillator phase, per the approach taken in 61 , to a value of 5.0 radians. This value was selected so that the model’s average predicted DLMO time across all participants aligned with the data.

Light data were input to the model in 1-h non-overlapping bins, using the maximum value in each bin. The light sequence was input repeatedly for 300 days to allow entrainment. For 21 of the 22 individuals this resulted in stable entrainment. For one Irregular individual, entrainment never occurred, even allowing 300 days to entrain, so no model-predicted DLMO could be obtained. Data were used from when individuals received the actiwatch to the day of saliva collection, with data truncated in cases where the individual was clearly not wearing the sensor. Average predicted DLMO time (first crossing of a 5 pg/mL threshold) was used as model DLMO. This approach was taken rather than predicting DLMO at the time of salivary collection, because not all individuals had usable light data up to that day.

This model generates outputs of the circadian clock and estimates of DLMO timing, allowing us to investigate the reasons for group differences in our primary outcome: DLMO timing. We note that this model does not generate sleep/wake patterns, as another recent model does 62 , 63 , as it was not designed for that purpose. We therefore did not include model predictions of sleep/wake patterns.

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Acknowledgements

We thank Omer Zaidi and Michael Shreeve for study coordination and Salim Qadri for diary programming. This research was supported by awards NIH R01-GM-105018, NIH R01-HL-114088, NIH R01-HL094654, NIH P01-AG09975, NIH K24-HL105664, NIH K99-HL119618, NIH R00-HL119618, NSBRI HFP0280, NSBRI HFP02802, and NSBRI HFP02801.

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Andrew J.K. Phillips and William M. Clerx contributed equally to this work.

Authors and Affiliations

Sleep Health Institute and Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA, USA

Andrew J. K. Phillips, William M. Clerx, Conor S. O’Brien, Laura K. Barger, Steven W. Lockley, Elizabeth B. Klerman & Charles A. Czeisler

Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA

Andrew J. K. Phillips, William M. Clerx, Laura K. Barger, Steven W. Lockley, Elizabeth B. Klerman & Charles A. Czeisler

Affective Computing Group, Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

Akane Sano & Rosalind W. Picard

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Contributions

A.J.K.P., W.M.C., C.A.C., E.B.K., S.W.L., and L.K.B. designed the research study. W.M.C. and C.S.O.B. conducted the experiments and acquired the data. All authors analyzed data and wrote the manuscript.

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Correspondence to Andrew J. K. Phillips .

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Competing interests.

L.K.B. has previously received research support from Cephalon, NFL charities, Sysco and San Francisco Bar Pilots. She has received consulting/lecture fees or served as a board member for Alertness Solution, Ceridian, Davis Joint Unified School Board, San Jose State University Foundation, Pugot Sound Pilots, Sygma and Torvec. RWP is a co-founder of and shareholder in Empatica Inc and Affectiva Inc and serves on the board of Empatica. She is inventor or co-inventor on over two dozen patents, mostly in the field of affective computing and physiological measurement. She has received royalty payments from MIT for patents licensed to Affectiva, consulting and honorarium payments from Merck, Samsung, Analog Devices, and fees for serving as an expert witness in cases involving wearable sensors from Apple and Intel. Her research is funded in part by a consortium that includes over 70 companies who fund the MIT Media Lab (up to date list is kept online at http://media.mit.edu ) and includes project funding supporting her team’s work from Robert Wood Johnson Foundation, The Simons Foundation, The SDSC Global Foundation, NEC, LKK, Cisco, Deloitte, Steelcase, and Medimmune. She has received travel reimbursement from Apple, Future of Storytelling, Mattel/Fisher-Price, Microsoft, MindCare, Motorola, Planetree, Profectum, Sentiment Symposium, Seoul Digital, Silicon Valley Entrepreneurs Network, and Wired. AJKP holds a patent related to estimating physiological states from measurements of sleep and circadian rhythms. SWL has received consulting fees from Perceptive Advisors, Carbon Limiting Technologies Ltd on behalf of PhotoStar LED, Serrado Capital, Atlanta Hawks; and has current consulting contracts with Headwaters Inc., Wyle Integrated Science and Engineering, PlanLED, Delos Living LLC, Environmental Light Sciences LLC, Hintsa Performance AG, Pegasus Capital Advisors LP, Akili Interactive, Focal Point LLC, OpTerra Energy Services Inc., and Light Cognitive. SWL has received unrestricted equipment gifts from Bionetics Corporation and Biological Illuminations LLC; has equity or stock options in iSLEEP, Pty, Melbourne, Australia and Akili Interactive; advance author payment and/or royalties from Oxford University Press; honoraria plus travel, accommodation or meals for invited seminars, conference presentations or teaching from Estee Lauder, Lightfair, and Informa Exhibitions (USGBC); travel, accommodation and/or meals only (no honoraria) for invited seminars, conference presentations or teaching from FASEB, Hintsa Performance AG, Lightfair, and USGBC. SWL has completed investigator-initiated research grants from Biological Illumination LLC and Vanda Pharmaceuticals Inc and has an ongoing investigator initiated grant from F. Lux Software LLC; completed service agreements from Rio Tinto Iron Ore and Vanda Pharmaceuticals Inc.; and completed three sponsor-initiated clinical research contracts from Vanda Pharmaceuticals Inc. SWL holds a process patent for the use of short-wavelength light for resetting the human circadian pacemaker and improving alertness and performance which is assigned to the Brigham and Women’s Hospital per Hospital policy (2005). SWL has also served as a paid expert on behalf of several public bodies on arbitrations related to sleep, light, circadian rhythms and/or work hours for City of Brantford, Canada, and legal proceedings related to light, sleep and health (confidential). SWL is also a Program Manager for the CRC for Alertness, Safety and Productivity, Australia. EBK received travel reimbursement from the Sleep Technology Council and has served as an expert witness in cases involving transportation safety and sleep deprivation. CAC has received consulting fees from or served as a paid member of scientific advisory boards for: Amazon.com, Inc.; A2Z Development Center; Bose Corporation; Boston Celtics; Boston Red Sox; Cephalon, Inc.; Citgo Inc.; Cleveland Browns; Columbia River Bar Pilots; Gerson Lehman Group; Institute of Digital Media and Child Development; Jazz Pharmaceuticals; Koninklijke Philips Electronics, N.V.; Merck & Co. Inc.; Minnesota Timberwolves; Novartis; Portland Trail Blazers; Purdue Pharma; Quest Diagnostics, Inc.; Samsung Electronics; Sleep Multimedia, Inc.; Teva Pharmaceuticals; Valero Inc.; Vanda Pharmaceuticals; and Zeo Inc.. CAC has also received education/research support from Cephalon Inc., Jazz Pharmaceuticals, Mary Ann & Stanley Snider via Combined Jewish Philanthropies, National Football League Charities, Optum, Philips Respironics, ResMed Foundation, San Francisco Bar Pilots, Schneider Inc., Simmons, Sysco and Vanda Pharmaceuticals, Inc. The Sleep and Health Education Program of the Harvard Medical School Division of Sleep Medicine (which CAC directs) has received Educational Grant funding from Cephalon, Inc., Jazz Pharmaceuticals, Takeda Pharmaceuticals, Teva Pharmaceuticals Industries Ltd., Sanofi-Aventis, Inc., Sepracor, Inc. and Wake Up Narcolepsy. CAC is the incumbent of an endowed professorship provided to Harvard University by Cephalon, Inc. and holds a number of process patents in the field of sleep/circadian rhythms (e.g., photic resetting of the human circadian pacemaker). Since 1985, CAC has also served as an expert on various legal and technical cases related to sleep and/or circadian rhythms including those involving the following commercial entities: Bombardier, Inc.; Continental Airlines; FedEx; Greyhound; Purdue Pharma, L.P.; and United Parcel Service (UPS). CAC owns or owned an equity interest in Apple, Lifetrac, Inc., Microsoft, Somnus Therapeutics, Inc., Vanda Pharmaceuticals, and Zeo Inc. He received royalties from CNN, McGraw Hill, Houghton Mifflin Harcourt, and Philips Respironics, Inc. for the Actiwatch-2 and Actiwatch-Spectrum devices. CAC’s interests were reviewed and managed by Brigham and Women’s Hospital and Partners HealthCare in accordance with their conflict of interest policies.

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Phillips, A.J.K., Clerx, W.M., O’Brien, C.S. et al. Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing. Sci Rep 7 , 3216 (2017). https://doi.org/10.1038/s41598-017-03171-4

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DOI : https://doi.org/10.1038/s41598-017-03171-4

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A qualitative content analysis of "problem students": how can we identify and manage them?

• Soleiman Ahmady 1 ,
• Nasrin Khajeali 2 ,
• Masomeh Kalantarion 3 &
• Mitra Amini 4  

BMC Research Notes volume  13 , Article number:  566 ( 2020 ) Cite this article

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Metrics details

Problem students is one of the important issues in medical education. This study aimed to identify the problem students and the ways for managing these students from the educational experts view. Purposive sampling was used, and data collection continued until data saturation was achieved. Data analysis was performed by the content analysis method based on the Heidegger approach. We interviewed 12 educational experts who had a history of dealing with "problem students”.

After data analysis, five main themes and 28 categories, and 164 codes were extracted. The reasons for changing a student to a problem students was: student self-regulation skills, multilayer interactions, curriculum failure, identification policy and supportive solutions. The results indicated that despite revision in the curriculum, there were shortcomings in identification and management of problem students. According to participants, existence of a comprehensive system and a capable counseling center can identify the problem student sooner. On the other hand by improving self-regulation skills, active teaching methods and frequent formative evaluation and the use of supportive strategies, problem student can be encouraged to complete their education successfully. This study emphasized faculty development, reviewing the faculty member recruitment, strengthening counseling centers, improving the exams.

Introduction

In some students, admission to the university causes cultural, social, and psychological deprivations and increases anxiety. In such cases, the student is unable to adapt efficiently and effectively and suffers from academic failure [ 1 ]. The academic decline is one of the problems of educational systems, including universities [ 2 ].

Vaughn et al. define a problem students as “a learner whose academic performance has declined significantly due to an emotional, cognitive, structural, or individual problem” [ 3 ]. It can ensue consequences such as addiction, apathy, anxiety, depression, and even suicide [ 2 ]. Problem students have various causes such as inappropriate teaching methods, improper use of teaching aids, the physical and social environment of the classroom, and the motivation of students and loneliness, family circumstances, and biological factors [ 4 ]. Also, students' academic failure is a major social problem, rather than a personal issue, which requires fundamental steps to solve [ 5 , 6 ]. Meanwhile, academic decline is of particular importance in regard to medical students due to their job sensitivity and direct relationship with people’s health [ 6 , 7 ].

Therefore, the problem students is of particular importance regarding medical students due to their job sensitivity and direct relationship with people's health [ 4 , 7 ]. In these students, dropping out will further entail poor performance in hospitals and medical centers, sometimes leading to irreversible conditions. Due to the importance of identifying these students, and little evidence for identifying and providing support for them, we aimed to explore how to identify and manage problem students among the students of Shahid Beheshti University of Medical Sciences. To the best of our knowledge, no research has been conducted on this issue in Iran; therefore, this study aimed to identify and manage "problem students" in medical education.

The data collection was performed via a one-on-one semi-structured. Semi-structured interview allow participants to show their opinions in their own words freely. It can provide valid and reliable data.

This qualitative approach was designed and implemented by a content analysis method [ 8 ].

Participants

The study population consisted of a director, experts in student support system, and the faculty members, who had a history of dealing with "problem students." Before starting the interview, the researcher built a good rapport with the participants and explained to the interviewees the purpose of the research. The study population consisted of director the Center for Education and Development of the Ministry of Health, the Deputy Minister of Education, the faculty members, a Faculty in Counseling Psychology, who had a history of dealing with “problem student”.

Data gathering

A member of the research team (N Kh) who is also has a history of studying with problem students, interviewed. Researchers used purposive sampling to provide their expert opinion on problem identification and management. Sampling continued until data saturation, and 12 respondents entered the study. One participants dropped out of the study because of didn’t have time. Each interview conducted in a private room in the medical school or Counseling center. We gathered additional information regarding age and sex from participants. The mean age of those participants was 52 years (range 42–62 years) and 77% were male and 23% were female.

To begin exploring the expert’ experiences, pilot study was conducted. The interviews were recorded by a tape recorder to increase the accuracy of data collection. Interviews lasted between 30 and 60 min. Filed note didn’t used.

Semi-structured interviews were done based on general questions such as the following questions:

Did you have any experience in dealing with problem students? How did you identify them? (May you define an objective example of coping with problem students?

What are the barriers and problems for problem students? Can you talk more about this?

In the current education system, what criteria (formal and informal) are problematic for comprehensive identification? By what criteria are they identified? What services do they receive?

What happens to them in the end?

What strategies are needed to be able to manage them?

What strategies are required to be able to support them?

Data analysis

The data were analyzed by the inductive qualitative content method [ 10 ]. After each interview, the interviews were immediately transcribed. Then, the text of the meetings was reviewed to gain a general understanding. After that, each summarized unit was abstracted and named with a code by two researchers. The codes were categorized based on similarities and how to merge them. 12 interview texts were examined and confirmed by two researchers. We didn’t use software for analyzing.

Trustworthiness

Dependability, the codes were checked with participants, and the accuracy of the information was examined.

Guba and Lincoln's criteria were used to achieve credibility and dependability [ 4 ]. Credibility, the interpretation and report (or a portion of it) were given to the members of the sample (informants) to check the authenticity of the work.

Furthermore, the findings were verified by external auditors familiar with qualitative research. It means that parts of the interview text, along with the relevant codes and classes produced by the two observer’s casual with qualitative research, were examined and confirmed. To make the findings transferable, we tried to transcribe the participants' sentences verbatim.

Ethical considerations

Participant's information was kept confidential to the researcher, and individuals had the option to withdraw from the study at any stage of the research.

Five main themes emerged in this study: self-regulation skills, multilayer interactions, curriculum failure, identification policy, and supportive solutions.

Self-regulation skills

This theme containing three more related categories including: self-awareness, low goal setting, and inability to describe oneself without judgment.

One participant ascertained: I think one of the significant challenges is that they don't have self-awareness, and that's why they're justifying it instead of rooting the problem out.

Multilayer interactions

This theme refers to the four categories: family role, peer role, hidden curriculum, and teacher-student relationships.

Family members both positively and negatively contribute to the prevention or creation of problematic students.

Participants said in this regard: "We have another problem. There are parents, called helicopter parents, according to our field. Helicopter parents are those who raise their children under the age of 18 in the lap of luxury. Then, they immediately bring their children to university and throw them down. They're spinning up. They're neither going to take our hand nor helping us to see what we should do. The story of our intervention is that the family has to intervene, but they are inefficient".

Participants in the study reported that the hidden curriculum and teacher-student relationships also played an essential role in creating or preventing problem learners.

Curriculum failure

The most common codes are presented in this theme. This could be described by three categories: inappropriate curriculum, ineffective teaching, and evaluation are the causes of curriculum failure.

One of the respondents said: teachers' inability to effectively communicate when teaching led to problem students.

A participant commented about the inappropriate curriculum: "For example, our problem is about the expected curriculum. Some students believe that the content of the medical curriculum is irrelevant to their future job needs. Hence, differences between the expected and experienced curricula sometimes make a huge discrepancy that can lead to disappointed students.

Identification policy

In this study, it has been stated that the identification system isn't systematic.

Some participants reported: some institutions identified a problem student based on formal criteria, while others identified informal group meetings, so there was no comprehensive identification policy.

Supportive strategies

In this category, supportive strategies have been proposed based on the role of family, peers, educational system, and teachers.

One participant said:"

In the educational system, in addition to assessing knowledge, we measure items such as study skills, learning style, etc., it can help us not have problem students.

Another participant reported:

The failure is desirable in some cases when the individuals have no talent in a particular field. So, these failures warn us to encourage the student to choose another field.

Some related themes and quotes are listed in the Table 1 .

This study aimed to explain the understanding of experts regarding the identification and management of problem medical students.

Self-regulation skill is the first theme of this study. Participants reported this skill is one of necessary skills for medical students. Guntern stated a significant relationship between self-regulatory skill and academic achievement [ 9 ].

Sobral [ 10 ] also showed that the more learning leads to reflective learning, the better one's academic achievement and preventing problem students. Reflective thinking, which is one of the sub-categories of self-regulatory skills [ 10 ].

Multilayer interactions was another theme in our study. Steinert points to the critical role of classmates and teachers in identifying and helping problem students [ 2 ]. If the teacher-student relationship is well established, educational goals will be achieved with more quality and ease. That was similar to our study.

In a case report of a dismissed medical student with a higher educational background, Aghaei Afshar emphasizes the role of the family in paying attention to their children's abilities and interests in identifying a problem student. On the other hand, the university counseling system should identify the endangered students especially at the beginning of the study and take care of them with maintain contact with the family [ 11 ].

Another important finding of the current study was the theme of curriculum failure in higher education. Participants cited the inappropriate curriculum, ineffective assessments, and lack of effective teaching methods as factors of curriculum failure. These results are consistent with Roos's study [ 12 ]. Therefore, curriculum should be tailored to the needs of the community, so students can provide valuable insights for the curriculum and this has impact on the learning process, which is essential for educational centers.

In the study of the graduates, one of the graduates was asked about the extent of the curriculum presented, only 4% agreed with the appropriateness of the curriculum to empower the physicians [ 13 ].

Regarding the theme of identification policy, the participants pointed out the lack of a comprehensive system of identification and ineffective intervention, consistent with the results of Shams [ 14 ]. Therefore, a global identification system and effective response are required to support problem students.

The last theme was about supportive strategies. In this regard, Students can be supported through family, peers, educational systems, and teachers. These strategies have been reported in Steiner's study [ 2 ].

Aghaei Afshar is highlighted in family’s role in identifying problem student in case of dismissed medical student [ 11 ].

The result of our study was in line with the results of other studies and had a more comprehensive view of problem student identification in comparison to previous studies because it examined various aspects of medical education such as curriculum planning, teaching methods, evaluation, support strategies and educational policy changes.

The present study emphasized that we could identify and manage the problem students with the best approach by faculty development, reviewing the faculty member recruitment, strengthening counseling centers, improving the exams, and screening the students on arrival.

Future research could focus on recognize the demographic psychological status of problem learner for identifying and increase their coping rate with effective interventions. We designed a qualitative study, so did not investigate the files of the non-problem students in the cohorts. So a full case–control study would be required.

Limitations

A limitation of the present study was that some participants did not allow to record conversations; hence, the researcher had to write down everything she heard. Another limitation was that the researcher made several appointments to interview the experts, but the interviews were postponed due to the professors' busy schedule.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors thank all of the authorities and teachers at medical School at Shahid Beheshti University of Medical Sciences for their assistance.

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Soleiman Ahmady

Fasa University of Medical Sciences, Fasa, Iran

Nasrin Khajeali

Department of Medical Education, Students Research Committee, Virtual School of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Masomeh Kalantarion

Clinical Education Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

Mitra Amini

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SA and NK contributed to the study, coordination, participated in the acquisition of data and drafted the manuscript. MK and MA participated in the acquisition of data and analysis and drafting the manuscript. All authors read and approved the final manuscript.

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Ahmady, S., Khajeali, N., Kalantarion, M. et al. A qualitative content analysis of "problem students": how can we identify and manage them?. BMC Res Notes 13 , 566 (2020). https://doi.org/10.1186/s13104-020-05393-8

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Home > ETD > ETD_MASTERAL > 1461

Master's Theses

Characteristics of regular and irregular high school students of ermita catholic school, manila.

Arul A. Arockiam

Date of Publication

Document type.

Master's Thesis

Degree Name

Master of Science in Guidance and Counseling

Subject Categories

Educational Assessment, Evaluation, and Research

Br. Andrew Gonzalez FSC College of Education

Department/Unit

Counseling and Educational Psychology

Thesis Adviser

Dr. Isagani Cruz

Defense Panel Chair

Dr. Buenaventura Medina

Defense Panel Member

Dr. Simplicio Bisa Dr. Eduardo Deveza

Abstract/Summary

Adolescents beset with problems of biological-physical and emotional-psychological changes, encounter various aspects of stress and pressures and fail in their academic performance. Studies have shown that there are various factors affecting one's academic performance. The non-intellective variables could prove influential and might have a strong effect but as reflected in the poor grades of students and in the high rate of failure they remain to be a problem. While non-intellective factors such as home environment, study habits, televiewing, peer influence and teaching method could account for academic performance, this study is very useful in the sense that some of the factors of academic failure are detected and analyzed. Being so, the effect of non-intellective factors on academic achievement had been extensively studied. However, due to the difficulty of measuring the non-intellective variables and for lack of instruments with which to assess them, studies on non-intellective factors have remained few. Therefore in this study the researcher focused his attention on gaining information regarding characteristics of regular and irregular students of Ermita Catholic School in terms of the intellective factor academic achievement and five selected non-intellective factors viz. home environment, study habits, televiewing, peer influence and teaching method. The students' GPA being the measure of academic achievements, the computerized grades issued at the end of the first and second quarters of the school year 1991-92 were used. A Non-Intellective Factors' Scale (NIFS) adapted from the Sia Academic Performance Scale (APS) was devised to measure the non-intellective factors of 100 regular students who had no failing grades and 115 irregular students who had incurred failures in at least one or more subjects during the first and second quarters.

Data analysis was done through the following: - Means and Standard deviation for items and factors - Frequency and Percentage for personal data - ANOVA for difference - For hypothesis testing the level of significance was set at .05 level. As hypothesized, there are characteristic difference between the regular and the irregular students of Ermita Catholic School, however, while statistically significant, the differences are not great. The findings of the study are discussed separately along with the conclusions and recommendations arising out of them are also given.

Abstract Format

Accession number, shelf location.

Archives, The Learning Commons, 12F Henry Sy Sr. Hall

Physical Description

140 leaves; 28 cm.

High school students; Catholic schools; Personality and academic achievement

Recommended Citation

Arockiam, A. A. (1992). Characteristics of regular and irregular high school students of Ermita Catholic School, Manila. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/1461

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Introduction to the Study

Statement of the Problem

Significance of the study, definition of terms, irregular students - time spent at college.

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What are the Challenges encounter of Irregular Students?  

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Effective coping strategies for irregular students can vary based on individual differences in sensitivity to stimuli. However, for undergraduate international students in Ghana, coping strategies for adjustment needs include planning, positive reassessment, social support, acceptance of responsibility, and self-control . These strategies aim to reduce stress and enhance productivity during challenging situations. Additionally, emotional intelligence plays a crucial role in increasing the effectiveness of coping strategies for students, particularly in educational settings. Implementing coping strategies tailored to the specific needs of irregular students, such as addressing feelings of language anxiety through practical coping mechanisms, can significantly improve their learning efficiency and overall well-being. It is essential to consider individual differences and tailor coping strategies to meet the unique needs of irregular students for optimal outcomes.

Factors contributing to the attendance of irregular students include various elements such as personal, school, and environmental factors . Student absenteeism can be influenced by factors like student attitude, family circumstances, and student activities . Additionally, reasons for irregular attendance can stem from issues like poor working conditions, lack of community ties, contract teaching, and inadequate supervision . Moreover, student absenteeism can be caused by a range of factors such as sickness, lack of school fees, delinquency, suspension, and even natural disasters . Understanding these diverse factors is crucial in addressing irregular attendance and its impact on academic performance in educational institutions.

An irregular student can be defined as a student who exhibits irregularity in attendance or behavior, impacting their academic performance. Factors contributing to student irregularity include employment, marital status, age, nature of subjects, and lecture effectiveness . Additionally, irregular students may struggle with visual processing issues, affecting their ability to receive and process information in a traditional classroom setting . Teacher irregularity is also a significant issue in educational institutions, with causes such as poor working conditions, lack of community ties, and absence of supervision . Nontraditional students, on the other hand, are older students with unique characteristics like independence, motivation, and life experience, pursuing higher education in a nontraditional manner . Understanding and addressing the factors contributing to student irregularity is crucial for enhancing academic performance and overall educational outcomes.

The challenges that irregular students face after switching strands include academic failure, poverty, and a lack of support from nonformal education systems when transitioning to formal high schools . Additionally, the switch to remote learning during the COVID-19 pandemic has led to increased stress, decreased ability to focus, and varying levels of prioritization of classes, which can impact performance . It is important to note that voluntary surveys may not capture the full extent of the detrimental effects of the shift to online instruction .

Irregular students can have a negative impact on class attendance. Students who are chronically absent not only receive fewer hours of instruction but also disrupt the learning environment for their peers . High rates of student absenteeism can affect regular attenders as well, as teachers must accommodate non-attenders in the same class . Research has shown that chronic absenteeism is associated with poor attendance and early school leaving . Additionally, a study found that having chronically absent classmates can have a detrimental effect on the academic achievement of other students in the same educational setting . Therefore, the presence of irregular students can lead to decreased attendance and potentially lower academic outcomes for the entire class.

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Transformational and situational leadership styles differ significantly in their approaches and impacts on organizational dynamics and employee performance. Transformational leadership is characterized by its focus on inspiring and motivating employees through a shared vision, fostering an environment of intellectual stimulation, and providing individualized consideration and support. This style has been shown to positively influence organizational performance, knowledge management capabilities, and employee engagement, as evidenced by studies in various sectors, including Islamic banks in the UAE and the healthcare sector . Transformational leaders are adept at creating a sense of purpose and meaning in work, which enhances job engagement and task performance through mechanisms such as task significance and job variety . This leadership style is also crucial in educational settings, where it supports deeper learning by addressing systemic factors like teacher collaboration and assessment practices, thereby facilitating broader adoption of innovative educational models . In contrast, situational leadership, which includes transactional and laissez-faire styles, adapts leadership behaviors based on the specific context and needs of the situation. Transactional leadership, for instance, is more effective in environments requiring clear structure and immediate feedback, such as during the COVID-19 pandemic, where continuous feedback helped mitigate performance declines among salespeople . This style focuses on contingent rewards and management-by-exception, which are critical in settings like the coal mining industry for the acceptance of new technologies . Laissez-faire leadership, on the other hand, is generally seen as less effective, often leading to negative outcomes such as increased role overload and conflict, which can exacerbate stress and intention to leave among employees . Situational leadership's flexibility allows leaders to switch between different styles, including democratic leadership, which balances leadership and freedom effectively in various scenarios . Additionally, the effectiveness of these leadership styles can vary based on demographic factors, such as age and gender, with transformational leadership being particularly effective among older participants and in homogeneous teams . Overall, while transformational leadership excels in fostering long-term vision and employee development, situational leadership's strength lies in its adaptability to immediate needs and specific contexts, making it a versatile approach in dynamic environments.

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"Minsan Nakaka-OP Siya": Being An Irregular Student Is Tougher Than It Seems

College… just saying it makes me want to cry and sleep at the same time. It's hard enough for a regular student, but what irregular students face on a daily basis can sometimes be harder to handle. Not only because they have to take more units than us or take classes twice, it's also due to other inevitable underlying factors that come along with it.  Josh Espinoza , a fourth-year marketing management student at San Beda University, shares his ups and downs on his journey as a current irregular student.

The reason behind becoming an irreg  

It began in the school year of 2017 when he transferred from UST CFAD as a Fine Arts student majoring in Advertising Arts to become a BSBA Marketing Management major at SBU. He shifted courses because he couldn't see his work receiving the grades it deserves. “It wasn't really worth it for me because I used to create plates as big as 15x20, or even bigger. I usually stay up for a total of two days with minimal food intake because I have to focus on finishing my plate, after doing that, I usually get a grade of 2.75 or even a 5.” He felt useless, unwelcomed, and unworthy. So he thought, if continuing the course he had in UST meant that he would endure negative feelings throughout college, it might just be better to shift courses despite knowing that he will be an irregular student.

You'll have to work extra hard because of your extra load.

A recurring problem that irreg students face is that the load they take every semester is often heavier than regular students’ since they are catching up on their classes. So, since then, time management became his friend in order to excel. And that is true, balancing everything and knowing what to prioritize can be overwhelming at times, but as you get used to it, it can help you be successful in your future endeavors.

It makes you feel O.P. sometimes.  

Besides catching up with classes for his new course, various problems started to latch on to him as well. In his first semester in SBU, he failed one of his classes and got mixed with a different block as he had to take it a second time around—which was a tough fate to go through since he did not know anyone from there. "I didn't know anyone... the profs they have, the blockmates of the section I enrolled in, no one. I  didn't know what I was going to do. When the first to the third week of classes came, I didn't know what they're talking about."

Adjusting to a new class is one of the prevalent dilemmas that irreg students encounter: Not knowing who to contact when you need help on your subjects, missing reminders often because you're not included in the class group chat, and witnessing the block’s bond and inside jokes and not being able to relate and laugh along. He shared, "It all went fine but it was so hard not being with your blockmates. You don't know what they are up to because they have a different schedule than you, you have no idea where you can reach for help since you're just fairly new to the other section. Everything was so confusing."

You'll feel unsure of yourself sometimes.

Intimidation also got the best of him along the way. Since he's new in the program and all of his classmates already had a working idea of what they are studying, he felt like he was below his peers.

" Siguro iyong time na nagsabay-sabay ang deadline in one day and I was so anxious to the point where I didn't want to pass it all because I wasn't really satisfied with the requirements I did. Then ‘ yun , I did [submit] my requirements but I flunked them all," he said.

That did not let him slow down his pace though. He began to change his perspective on his situation into a more positive view and changed his behavior towards the hardships he encountered.

You’ll find your way around it.

He knew that the only way out of his misery was to change his attitude towards it. He shared what he did to adapt, "I worked on building connections with other people outside my block [since I realized we could] help one another pass the subject rather than compete with each other."

College is not about who's on top and who's not. It's breaking your barriers to explore and create connections that can help you grow not only as a student, but as an individual. Take it from Josh's experience—"I've learned how to compromise and mingle with others. It really takes time for other people to get used to what they have or what they are given..." This shows that taking the time to know how you can fix the problem is better than just letting it be that way and going with the flow. You can't move forward until you make an effort to get out of that deadly zone.

Just a reminder from Josh to his fellow irreg students, "Don't lose hope if you flunked a subject or two... there's still someone willing to help you get through whatever problems you're having. Try to build connections in your school because it'll be a huge help to your college and work life in the future."

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Generative AI Can Harm Learning

59 Pages Posted: 18 Jul 2024

Hamsa Bastani

University of Pennsylvania - The Wharton School

Osbert Bastani

University of Pennsylvania - Department of Computer and Information Science

Özge Kabakcı

International Business School - Budapest (IBS)

Rei Mariman

Independent; Independent

Date Written: July 15, 2024

Generative artificial intelligence (AI) is poised to revolutionize how humans work, and has already demonstrated promise in significantly improving human productivity. However, a key remaining question is how generative AI affects learning , namely, how humans acquire new skills as they perform tasks. This kind of skill learning is critical to long-term productivity gains, especially in domains where generative AI is fallible and human experts must check its outputs. We study the impact of generative AI, specifically OpenAI's GPT-4, on human learning in the context of math classes at a high school. In a field experiment involving nearly a thousand students, we have deployed and evaluated two GPT based tutors, one that mimics a standard ChatGPT interface (called GPT Base) and one with prompts designed to safeguard learning (called GPT Tutor). These tutors comprise about 15% of the curriculum in each of three grades. Consistent with prior work, our results show that access to GPT-4 significantly improves performance (48% improvement for GPT Base and 127% for GPT Tutor). However, we additionally find that when access is subsequently taken away, students actually perform worse than those who never had access (17% reduction for GPT Base). That is, access to GPT-4 can harm educational outcomes. These negative learning effects are largely mitigated by the safeguards included in GPT Tutor. Our results suggest that students attempt to use GPT-4 as a "crutch" during practice problem sessions, and when successful, perform worse on their own. Thus, to maintain long-term productivity, we must be cautious when deploying generative AI to ensure humans continue to learn critical skills. * HB, OB, and AS contributed equally

Keywords: Generative AI, Human Capital Development, Education, Human-AI Collaboration, Large Language Models

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Joint Safety Team featured in ACS Chemical Health & Safety

MINNEAPOLIS / ST. PAUL (7/19/2024) – The Joint Safety Team (JST) recently published their successes in the American Chemical Society's Chemical Health and Safety Journal. The JST, a student group made up of graduate students and postdoctoral associates from the Departments of Chemistry and Chemical Engineering and Materials Science, has been keeping the chemistry community at the University of Minnesota safe for 10 years. 

The paper, titled "Community Connections Committee: How the Joint Safety Team of the University of Minnesota Innovates Promoting Vertical Safety Engagement" focuses on the JST's Community Connections Committee (CCC). The CCC aims to engage with the larger scientific community by connecting with high school science instructors, early stage researchers at primarily undergraduate institutions (PUIs), and local chemical industries. 

Read the full paper here

Vilma Brandao and Gretchen Burke, both Chemistry PhD and JST student leaders wrote: "The University of Minnesota JST is continuously gaining recognition as a leading student safety team in universities across the country, and we are thrilled to be part of this work and see that our Community Connections Committee work has been recognized at this level. The CCC is a unique idea, the first example of an outreach focus within a lab safety team. It connects our graduate students to high school teachers, PUI professors and students, and industry professionals, each collaboration engaging in chemical safety in a unique way. It is an organization created to spread safety knowledge and connect our work, as in the name, with the broader chemistry community. It is great to see the committee's achievements in its first two years be recognized in such a way, and exciting to see the committee continue to evolve - this year, the group is hosting the third annual high school teacher safety workshop in August, and organized a tour of Sherwin Williams labs this spring." 

Joint Safety Team

The Joint Safety Team is a researcher-led organization focused on improving the culture of safety in chemical laboratories at the University of Minnesota. The JST consists of graduate student or postdoctorate Laboratory Safety Officers (LSOs) within the Departments of Chemistry and Chemical Engineering and Materials Science. LSOs support the safety of their labmates by translating safety knowledge to their respective labs, liaising between the JST and the research group, directing lab members to the right resources for safety questions, and partaking in JST events. 

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Research on spatial developable mechanism considering revolute clearance joints with irregular rough surfaces.

1. Introduction

2. consider joint contact modeling for irregular rough surfaces, 2.1. contact state determination, 2.2. normal contact force model, 2.3. tangential friction force model, 2.4. model verification, 3. considering the dynamic modeling of space expansion mechanism of irregular rough surface joints, 4. the dynamic characteristic analysis of the expansion mechanism, 4.1. influence of clearance contact surface roughness on system motion, 4.2. analysis of the influence of rough clearance surface clearance size on system motion parameters, 5. conclusions.

• To solve the problem that a single-contact model has difficulty describing the dynamic contact process, the contact stiffness was modified based on the rough surface hypothesis, and then the hystere-damping factor was introduced to consider the energy dissipation. An improved normal contact force model based on the dynamic contact stiffness was established, and a joint contact model considering irregular rough surfaces was constructed by combining the modified friction model. Compared to the G-W model and finite element model, it is concluded that the modified model is closer to the finite element model and has higher accuracy than the traditional G-W model.
• Based on the established modified model, simulation research was conducted on the development mechanism, and the influence law of different contact surface roughness conditions and clearance sizes on the dynamics of the development mechanism was obtained. The dynamic response of different spacecraft components under different contact parameters was compared and analyzed, and the optimal processing parameters of components with different requirements of the space development mechanism were obtained. The optimal processing parameters can satisfiy the requirements of stability, response speed, and shape retention accuracy of the space development mechanism at the same time and provide a theoretical basis for the fine design of spacecraft.
• In the actual work process, the linkage of the mechanism is not in a rigid body state for a long time, so the rigid–flexible coupling expansion mechanism model could be established considering the flexibility of the linkage so as to describe the behavior of the expansion mechanism in practical engineering applications more accurately.
• This paper only focused on the dynamic modeling and dynamic response analysis of the two-stage deployable mechanism. With the continuous improvement of spacecraft functional requirements, the scale requirements of the space deployable mechanism will also increase, so it is urgent to carry out the dynamic modeling research of the multistage superimposed spatial developable mechanism. It provides a more perfect theoretical basis for the dynamic analysis and control of a large-scale multistage spatial expansion mechanism system.

Author Contributions

Data availability statement, acknowledgments, conflicts of interest.

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Share and Cite

Wang, J.; Zhang, H.; Wang, W.; Qi, C.; Xu, J.; Zhao, Y.; Ma, C.; Tian, J. Research on Spatial Developable Mechanism Considering Revolute Clearance Joints with Irregular Rough Surfaces. Actuators 2024 , 13 , 274. https://doi.org/10.3390/act13070274

Wang J, Zhang H, Wang W, Qi C, Xu J, Zhao Y, Ma C, Tian J. Research on Spatial Developable Mechanism Considering Revolute Clearance Joints with Irregular Rough Surfaces. Actuators . 2024; 13(7):274. https://doi.org/10.3390/act13070274

Wang, Junyu, Huibo Zhang, Wenyu Wang, Chaoqun Qi, Jianan Xu, Yang Zhao, Chao Ma, and Jian Tian. 2024. "Research on Spatial Developable Mechanism Considering Revolute Clearance Joints with Irregular Rough Surfaces" Actuators 13, no. 7: 274. https://doi.org/10.3390/act13070274

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